Techniques for efficient data categorization are disclosed herein. An example computer-implemented method includes receiving (i) a data set including a plurality of data points that each include at least one data line and (ii) a rule group including a plurality of rules and a plurality of rule sets. The example method further includes applying a categorization algorithm to the data set and the rule group that includes: generating a rule signature for each data line in each data point, identifying a set of unique rule signatures within the generated rule signatures, and determining a categorization for each unique rule signature of the set of unique rule signatures. The example method further includes storing a data object indicative of the determined categorizations.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
2.
NATURAL LANGUAGE PROCESSING TECHNIQUES FOR MACHINE-LEARNING-GUIDED SUMMARIZATION USING HYBRID CLASS TEMPLATES
As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations for generating guided summaries using summarization templates that are mapped to hybrid classes of a hybrid classification space for a hybrid classification machine learning model. In some embodiments, by using summarization templates, a proposed summarization framework is able to vastly reduce the computational complexity of performing summarization on an input document data object, such as an input multi-party communication transcript data object, by defining the set of dynamic data fields that apply to the input document data object based at least in part on an assigned class/category of the input document data object.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing temporally dynamic location-based predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform temporally dynamic location-based predictive data analysis by using at least one of cohort generation machine learning models and cohort-based growth forecast machine learning models.
Systems and methods are disclosed for determining authenticity of a resource system. The method includes receiving a dataset that includes a first subset and a second subset associated with a first resource system; down-sampling the first subset but not the second subset; generating a first feature for a machine learning model based on the down-sampled first subset; generating a second feature for the machine learning model based on the second subset; generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure; and initiating performance of one or more prediction-based actions in response to the generating.
Various embodiments of the present disclosure provide automated encryption and data de-identification techniques for improving computer security. The techniques apply machine learning and encryption techniques to transform input data objects to tagged data objects that may be locally decrypted using encrypted element representation stored within the tagged data objects. The techniques may include determining a protected data element from an input data object based on privacy criteria and generating the tagged data object from the input data object by replacing the protected data element with an anonymized privacy tag that identifies a privacy type of the protected data element. The techniques may further include generating an encrypted element representation of the protected data element and inserting the encrypted element representation to a portion of the tagged data object to enable decryption of the tagged data object by authorized entities.
36 - Financial, insurance and real estate services
Goods & Services
Pharmaceutical cost management services and drug utilization review services Pharmacy benefit management services; Providing counseling and consulting in the field of healthcare insurance benefits; Providing insurance information in the field of employee pharmacy benefit plans insurance
7.
Automated data routing and comparison systems and methods for identifying and implementing an optimal pricing model
To automate a pricing strategy for an otherwise unpriced service or item, prices may be generated through a plurality of different pricing models, via a pricing engine passing input data to a plurality of discrete pricing models. Those pricing models may pass data back to the pricing engine, which then adjudicates the results of the pricing models to identify a most-relevant pricing model for the particular unpriced service or item.
36 - Financial, insurance and real estate services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Insurance services, namely, underwriting, issuance and administration of health insurance insurance; Providing insurance information in the field of health insurance Providing healthcare information; Healthcare
Certain embodiments are directed to systems and methods for automatically providing data indicative of one or more characteristics of services that may be recommended to a particular patient, wherein the services are executable at least in part electronically based on data generated and provided by a system for facilitating access to the services. The generated data may be utilized for generating one or more user interfaces providing data regarding derived standard pricing data that is automatically assigned to the referred services and which may be attributable to a patient based at least in part on the patient's usage of the services.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
36 - Financial, insurance and real estate services
Goods & Services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
12.
MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating (i) a first label set representative of a selection of a content item based on search session data and (ii) a second label set representative of one or more transactions associated with the content item based on transaction data. A dominant label set is determined from the plurality of label sets based on an occurrence frequency associated with the first label set and the second label set. Based on an occurrence of an event associated with the dominant label set, either a first label associated with the dominant label set is assigned to first search query-content item record pairs associated with a training dataset, or one or more stochastic labels from the plurality of label sets are assigned to second search query-content item record pairs associated with the training dataset.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing content item suggestions based on historical search data of a user by: generating one or more content item feature vectors associated with a plurality of content items from a list of suggestions, generating one or more personalized feature vectors associated with the user based on user activity data, generating a plurality of predictions for the plurality of content items based on the one or more keyword feature vectors and the one or more personalized feature vectors, assigning a plurality of rankings to the plurality of content items based on the plurality of prediction probabilities, and generating one or more suggestions based on the plurality of rankings.
Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score. A presentation of the investigative targets is displayed via a graphical user interface of a device.
36 - Financial, insurance and real estate services
Goods & Services
Charitable services, namely, coordination of the procurement and distribution of in-kind donations of children's books, toys and games from public donors to hospitalized children Charitable foundation services, namely, providing financial support to to families with children for healthcare services that are not covered by health insurance
36 - Financial, insurance and real estate services
Goods & Services
Charitable services, namely, coordination of the procurement and distribution of in-kind donations of children's books, toys and games from public donors to hospitalized children Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
18.
PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM
There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system.
36 - Financial, insurance and real estate services
Goods & Services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
36 - Financial, insurance and real estate services
Goods & Services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
36 - Financial, insurance and real estate services
Goods & Services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
41 - Education, entertainment, sporting and cultural services
Goods & Services
Employment counseling and recruiting services; providing
on-line employment information regarding recruiting, career
advice, career events, resume advice, interview tips, job
resources, job listings, and internships; providing an
interactive website offering recruitment, employment, and
career development information (Term considered too vague by
the International Bureau pursuant to Rule 13 (2) (b) of the
Regulations); providing an on-line searchable database
featuring employment opportunities and content about
employment opportunities (Term considered too vague by the
International Bureau pursuant to Rule 13 (2) (b) of the
Regulations); employment counseling, recruiting and job
placement information and assistance for military veterans. Providing e-mail services that allows for individuals to
speak directly with an employment recruiter; providing live
online chat rooms that allows for individuals to speak
directly with an employment recruiter. On-line journals, namely, blogs featuring career advice
related to job opportunities, job resources and listings,
resumes, interviews, internships, college applications, and
general career-related information (Term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); education services, namely,
providing on-line presentations, seminars and
non-downloadable webinars in the field of career coaching,
resume drafting, interviewing skills, and job attainment.
25.
SYSTEMS AND METHODS FOR AUTOMATED DIGITAL IMAGE SELECTION AND PRE-PROCESSING FOR AUTOMATED CONTENT ANALYSIS
Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.
G06F 18/2137 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
G06T 7/70 - Determining position or orientation of objects or cameras
G06T 7/90 - Determination of colour characteristics
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
26.
MACHINE LEARNING TECHNIQUES FOR FEATURE PREDICTION BASED ON CLUSTERING USING ANCILLARY AND LOCATION DATA
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for a predictive data analysis system that is configured to rank one or more candidate entities. A machine learning model is trained to rank the one or more candidate entities for initiating the performance of one or more prediction-based actions based on one or more sets of a plurality of clusters generated based on population data merged with ancillary data, and an association of location data with external domain data. The plurality of clusters is generated by generating embeddings for one or more features associated with a plurality of entities selected for clustering and determining a similarity score for entity pairs selected from the plurality of entities based on a distance function and the embeddings.
An example system for parsing and transforming input data that includes processing circuitry and memory, the memory configured to store the input data. The processing circuitry is configured to determine a first delimiter in the input data. The processing circuitry is configured to determine a plurality of second delimiter hypotheses and parse the input data according to the first delimiter and the plurality of second delimiter hypotheses to generate a plurality of tables that are each associated with a respective one of the plurality of second delimiter hypotheses. The processing circuitry is configured to determine a respective consistency score for each of the plurality of tables and select a table from among the plurality of tables based on the respective consistency score associated with the table. The processing circuitry is configured to format the input data based on the selected table to generate formatted data and output the formatted data.
Various embodiments of the present disclosure provide machine learning techniques for transforming disparate, third-party datasets to canonical representations. The techniques include generating, using a machine learning prediction model, a canonical representation for an input dataset. The machine learning prediction model is previously trained using permutative input embeddings for a training dataset based on canonical data entity features, such that each permutative input embedding corresponds to a different sequence of the canonical data entity features. The permutative input embeddings are leveraged to generate a latent representation for the training dataset. The latent representation is combined with a canonical data map to generate an alignment vector, which is refined to generate an output vector for the input dataset. The machine learning prediction model is trained using a model loss generated based on a comparison of the output vector with a corresponding labeled vector.
Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score. A presentation of the investigative targets is displayed via a graphical user interface of a device.
36 - Financial, insurance and real estate services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
45 - Legal and security services; personal services for individuals.
Goods & Services
Providing insurance information Health care; Health care services, namely, wellness programs; Health counseling; Managed health care services; Providing information in the fields of health and wellness Providing patient advocate services in the field of health care
31.
MACHINE LEARNING MODEL TRAINING FOR IMPROVING ANOMALY DETECTION
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving machine learning model training based on receiving labeled training data objects, generating a normal prediction loss parameter, generating a global classification loss parameter, generating a composite loss parameter, and initiating the performance of one or more prediction-based operations.
36 - Financial, insurance and real estate services
Goods & Services
Insurance claims auditing services Claims administration services in the field of health insurance; Insurance consulting in the field of health insurance; Providing information about healthcare insurance plans
33.
PROMPT ENGINEERING AND AUTOMATED QUALITY ASSESSMENT FOR LARGE LANGUAGE MODELS
Various embodiments of the present disclosure provide prompt engineering and text quality assessment techniques for improving generative text outputs. The techniques may include identifying an initial document subset for a generative text request that includes a request to generate a generative text document based on one or more request text fields. The techniques may include generating a contextual classification for the one or more request text fields and identifying a refined document subset based on the contextual classification. The techniques may include generating one or more request field embeddings respectively corresponding to the one or more request text fields and identifying a prompt document subset based on the one or more request field embeddings. The techniques may include generating, using a large language model, one or more generative text fields using a generative model prompt based on the prompt document subset and the one or more request text fields.
Various embodiments of the present disclosure provide machine learning techniques for transforming third-party coding sets to universal canonical representations. The techniques may include receiving a plurality of training datasets corresponding to a plurality of predictive categories and generating a plurality of teacher models respectively corresponding to the plurality of predictive categories based on the plurality of training datasets. The techniques include generating a multi-headed composite model based on a plurality of trained parameters for each of the plurality of teacher models. The multi-headed composite model includes a plurality of model heads that respectively correspond to the plurality of teacher models and the plurality of predictive categories. The multi-headed composite model is leveraged to generate an output embedding for a text input of any predictive category. Each text input is processed by selecting a particular head of the multi-headed composite model that corresponds to the predictive category of the text input.
Various embodiments of the present disclosure provide machine learning techniques for transforming third-party coding sets to universal canonical representations. The techniques may include receiving a plurality of training datasets corresponding to a plurality of predictive categories and generating a plurality of teacher models respectively corresponding to the plurality of predictive categories based on the plurality of training datasets. The techniques include generating a multi-headed composite model based on a plurality of trained parameters for each of the plurality of teacher models. The multi-headed composite model includes a plurality of model heads that respectively correspond to the plurality of teacher models and the plurality of predictive categories. The multi-headed composite model is leveraged to generate an output embedding for a text input of any predictive category. Each text input is processed by selecting a particular head of the multi-headed composite model that corresponds to the predictive category of the text input.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving question-answer (QA) machine learning model training based on generating predicted label indicators, generating prediction score indicators and prediction explanation indicators, generating structured label-explanation datasets, generating synthetic QA training datasets, generating a prediction output, and initiating the performance of one or more prediction-based operations based on the prediction output.
Various embodiments of the present invention introduce technical advantages related to computational efficiency and storage efficiency of training reinforcement learning models using model-based reinforcement learning approaches. For example, various embodiments of the present invention enable training components of a dynamics model of a reinforcement learning framework using cross-space likelihood similarity measures between predicted transition likelihood models and empirical transition likelihood models even when the two noted likelihood models have distinct distribution supports. This enables using training/empirical observation data to train dynamics model components even when the output state spaces of the dynamics model components are distinct from the output state space of the empirical distributions determined using the training/empirical observation data.
Systems and computer-implemented method for evaluating programs are disclosed. A computer-implemented method includes determining a propensity score, using a propensity score model, for each patient among multiple patients. The multiple patients include treatment patients and control patients, and the propensity score represents a probability of assignment to a treatment group. The method includes assigning a random value to each patient in an assignment group. The assignment group includes at least one of the treatment patients or the control patients. The method includes sorting the patients based on the assigned random values and matching, based on the sorted patients and the determined propensity scores, each treatment patient to a control patient to create multiple matches. Each match includes one treatment patient and at least one control patient. The method includes performing, based on the multiple matches, one or more actions related to the multiple patients.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
39.
ADAPTIVE PREDICTIONS BASED ON CONTINUOUS SENSOR MEASUREMENTS
Various embodiments of the present disclosure provide predictive modeling techniques for generating predictive classifications from a plurality of continuous sensor measurements. The techniques may include identifying change points from sensor measurements for an input data object, determining data spikes from the sensor measurements based on the change points, and generating a predictive classification for the input data object based on the data spikes. The predictive classification may correspond to an evaluation time period with one or more sub-time periods. The techniques may include determining a sub-time period classification for each of the sub-time periods of the evaluation time period. The predictive classification may be derived from the sub-time period classifications. Using the predictive classification, an action output may be generated and provided for the input data object.
Systems and methods are disclosed for predicting a next text. A method may include receiving one or more documents, such as a document associated with a healthcare provider. The document is then processed to generate one or more tokens which are representative of the document. The document is then processed with a machine-learning model, such as a topic model, and a topic vector is output for the document. Based at least in a part on this topic vector, the document is then processed by one or more expert machine-learning models, which each output a probability vector. The various probability vectors are then further processed to calculate a total probability vector for the document. Based at least in part on the total probability vector for the document, a text output is selected.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a prediction output comprising one or more actions by receiving data associated with encounters in a tuple form, tokenizing the encounters, training a causal transformer machine learning model configured to predict outcomes of actions by translating action tokens from the tokenized encounters into one or more embedding spaces, and training a causal transformer machine learning model to select the one or more actions based on embeddings from the one or more embedding spaces.
Embodiments provide for application of personalized or individualized sensor-based risk profiles for impacts of external events. An example method includes receiving sensor data from one or more sensors couplable with a subject body of a subject population comprising a plurality of subject bodies; receiving external factor data associated with the subject population; generating a population-level external event impact metric, where the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population; generating a subject-level external impact metric, where the subject-level external event impact metric represents a predicted impact of the one or more external events on the physiological or other metric associated with the subject body; and initiating the performance of one or more prediction-based actions based on the subject-level external event impact metric.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
43.
MACHINE LEARNING TECHNIQUES FOR PREDICTING CLASSIFICATION PROGRESSION
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for predicting progression of condition classifications using a progression prediction machine learning model. The progression prediction machine learning model is trained using training data that assigns an outcome label to each entity that is in a defined base cohort based at least in part on whether the entity has subsequent severity level that exceeds an initial severity level. Once trained the progression prediction machine learning mode is configured to predict a severity level escalation probability in a future time period for an entity.
Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may generate a plurality of multidimensional patient-drug tensors based at least in part on a plurality of patient record data objects and a plurality of combined drug input vectors, generate an interaction-attentive prediction data object based at least in part on the plurality of multidimensional patient-drug tensors and at least one interaction-attentive machine learning model, generate an interaction-inattentive prediction data object based at least in part on the plurality of patient record data objects and an interaction-inattentive machine learning model, determine a drug combination indicator based at least in part on comparing the interaction-attentive prediction data object and the interaction-inattentive prediction data object, generate a predicted multi-drug contraindication data object based at least in part on the drug combination indicator, and perform one or more prediction-based actions.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
45.
Systems and methods for training multi-armed bandit models
A method for determining a treatment recommendation using a multi-armed bandit (MAB) model can include receiving first patient information, determining, using the MAB model, the treatment recommendation based on the first patient information, wherein the MAB model is trained based on a MAB treatment recommendation determined by the MAB model using second patient information and a clinical treatment recommendation determined according to clinical guidelines based on the second patient information, and providing the treatment recommendation.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
Systems and methods are disclosed for comparing a plurality of models. The method includes generating raw scores for the plurality of models based on multiple measures of demographic bias and performance. The raw scores for each of the plurality of models are stored in corresponding locations of a raw score matrix. The rank scores for the plurality of models are determined based on comparing the raw scores of the plurality models in each of the multiple measures of demographic bias and performance. The rank scores for each of the plurality of models are stored in corresponding locations of a rank matrix. Tournament scores for the plurality of models are determined based on performing a pairwise comparison of the rank scores. The tournament scores are stored in corresponding locations of a tournament matrix. The tournament scores are tallied to determine a rank for each of the plurality of models.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing cross-temporal search result predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform cross-temporal search result predictions using a multimodal hierarchical attention machine learning framework.
45 - Legal and security services; personal services for individuals.
Goods & Services
Health care cost containment Providing patient advocate services in the field of pharmacies and the process of obtaining prescription drugs at affordable prices
49.
PROCESSING DIFFERENT TIMESCALE DATA UTILIZING A MODEL
Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.
Various embodiments of the present disclosure disclose a machine learning training approach for intelligently training a plurality of machine learning models associated with a multitask environment. The techniques include jointly training the plurality of machine learning models based on task similarities by generating a similarity matrix corresponding to a plurality machine learning models, generating a sharing loss value for the at least two machine learning models, generating, using a loss function and a training dataset, a prediction loss value for a particular machine learning model of the at least two machine learning models, generating an aggregated loss value for the particular machine learning model based on the similarity matrix, the sharing loss value, and the prediction loss value, and updating the particular machine learning model based on the aggregated loss value for the particular machine learning model.
Various embodiments of the present disclosure provide signal interpretation and data aggregation techniques for generating predictive insights for a user. The techniques may include receiving initial physiological features for a user that are based on recorded sensor values for the user. The techniques include generating activity encodings for the user based on interaction data objects for the user and generating a combined input feature vector by aggregating the initial physiological features and the activity encodings. The techniques include generating, using a machine learning model, a physiological prediction for the user based on the combined input feature vector.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
52.
Automated data routing and comparison systems and methods for identifying and implementing an optimal pricing model
To automate a pricing strategy for an otherwise unpriced service or item, prices may be generated through a plurality of different pricing models, via a pricing engine passing input data to a plurality of discrete pricing models. Those pricing models may pass data back to the pricing engine, which then adjudicates the results of the pricing models to identify a most-relevant pricing model for the particular unpriced service or item.
Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.
Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.
Various embodiments of the present disclosure disclose machine-learning based data augmentation and prediction techniques for generating predictive classifications based on temporal data. A machine-learning based model is provided that can receive an input data object associated with a plurality of predictive temporal parameters; determine augmented temporal data objects based on the predictive temporal parameters; generate predictive data representations for the input data object based on the predictive temporal parameters and the augmented temporal data objects; generate a multi-channel predictive data representation based on the predictive data representations for the input data object; and generate a predictive classification for the input data object based on the multi-channel predictive data representation.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
56.
Programmatically managing social determinants of health to provide electronic data links with third party health resources
Certain embodiments are directed to systems and methods for automatically providing data indicative of one or more characteristics of services that may be recommended to a particular patient, wherein the services are executable at least in part electronically based on data generated and provided by a system for facilitating access to the services. The generated data may be utilized for generating one or more user interfaces providing data regarding derived standard pricing data that is automatically assigned to the referred services and which may be attributable to a patient based at least in part on the patient's usage of the services.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
57.
AUTOMATED ELECTRONIC MEDICAL RECORD (EMR) ANALYSIS VIA POINT OF CARE COMPUTING SYSTEMS
A care estimate module operating on a central computing entity receives a trigger indication (a) comprising patient identifying information and (b) that identifies a service; extracts the patient identifying information from the trigger indication; determines the service; identifies a potential provider that provides the service; and determines a care estimate for the potential provider to provide the service to the patient. The potential provider is identified based on eligibility information associated with the patient, a location associated with the patient, and an address associated with the potential provider. The care estimate is determined based on the eligibility information associated with the patient. The central computing entity generates a care estimate notification that identifies the potential provider and comprises the care estimate; and provides the care estimate notification such that a user computing entity receives the care estimate notification.
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/00 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Business management consulting and advisory services for the healthcare industry; Business services provided to the healthcare industry, namely, the collection, reporting, and analysis of healthcare quality data for business purposes; Medical practice management for others; Physician referrals
There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction. A method for generating the summary of a multi-party interaction includes receiving a multi-party interaction transcript data object comprising a plurality of interaction utterances from at least two participants; using an extractive summarization model to identify a key sentence of the multi-party interaction transcript data object; identifying an interaction utterance from the multi-party interaction transcript data object that corresponds to the key sentence; generating a contextual summary for the multi-party interaction transcript data object based at least in part on the interaction utterance; and generating a reported contextual summary for the multi-party interaction transcript data object based at least in part on the contextual summary.
There is a need for more effective, efficient, and accurate computer text comprehension. This need is addressed by applying unique text processing techniques to identify and remove irrelevant sentences from a narrative. The text processing techniques include a machine-learning based model that is trained using automatically generated training data that is tailored to a particular circumstance. A method for machine narrative comprehension includes receiving a narrative data object comprising one or more sentences; determining, using a machine-learning based irrelevant classifier model, a relevance of at least one of the one or more sentences; responsive to a determination that at least one sentence is irrelevant, generating a pertinent summary by removing the at least one sentence from the narrative; and generating, based at least in part on the pertinent summary, an output indicia data object for the narrative data object.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.
G06F 16/387 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
64.
SEARCH ANALYSIS AND RETRIEVAL VIA MACHINE LEARNING EMBEDDINGS
N N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.
As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations for generating guided summaries using summarization templates that are mapped to hybrid classes of a hybrid classification space for a hybrid classification machine learning model. In some embodiments, by using summarization templates, a proposed summarization framework is able to vastly reduce the computational complexity of performing summarization on an input document data object, such as an input multi-party communication transcript data object, by defining the set of dynamic data fields that apply to the input document data object based at least in part on an assigned class/category of the input document data object.
Various embodiments of the present invention improve the speed of training classification-based machine learning models by introducing techniques that enable efficient parallelization of such training routines while enhancing the accuracy of each parallel implementation of a training routine. For example, in some embodiments, a classification-based machine learning model is trained via executing N parallel processes each executing a portion of a training routine, where each parallel process is performed using a training set having a uniform distribution of labels associated with the classification-based machine learning model. In this way, each parallel process is more likely to update parameters of the classification-based machine learning model in accordance with a holistic representation of the training data, which in turn improves the overall accuracy of the resulting trained classification-based machine learning models while enabling parallel training of the classification-based machine learning model.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.
Example devices and techniques are described for personalizing a health-related search. An example computing device includes a memory and one or more processors circuitry. The memory is configured to store a search query. The one or more processors are configured to obtain the search query and determine that the search query is health related. The one or more processors are configured to, based on the determination that the search query is health related, determine a subject of the search query. The one or more processors are configured to determine health information of the subject of the search query. The one or more processors are configured to modify, based on the health information of the subject of the search query, at least one of the search query or an order of search results, and present the search results or the modified search results to the user.
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using distributed database update management techniques that utilize at least one of a field value temporal scoring machine learning model, total field utility measures, and distributed database update routines.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing context-based document classification prediction using a hierarchical attention-based keyword classifier machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform context-based document classification prediction using at least one of techniques using contextual keyword classifications, techniques using attention-based keyword classifier machine learning framework, techniques using a greedy matching indicator, and/or the like.
Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.
A61M 21/02 - Other devices or methods to cause a change in the state of consciousnessDevices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
73.
Machine learning techniques for parasomnia episode management
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.
G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
74.
MACHINE LEARNING TECHNIQUES FOR PARASOMNIA EPISODE MANAGEMENT
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.
G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
75.
Machine learning techniques for determining predicted similarity scores for input sequences
Systems and methods for dynamically generating a predicted similarity score for a pair of input sequences. A predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a target region match indication for the pair of input sequences, a fuzzy match score for the pair of input sequences, a character-level match score for the pair of input sequences, one or more similarity ratio occurrence indicators for the pair of input sequences, and a harmonic mean score of the fuzzy match score for the pair of input sequences and the token-level similarity probability score for the pair of input sequences.
36 - Financial, insurance and real estate services
Goods & Services
Claims administration services in the field of health insurance; Insurance consulting in the field of health insurance; Insurance services, namely, underwriting, issuance and administration of health insurance; Providing information about healthcare insurance plans
77.
PERSONALIZED DETERMINATION OF DRUG CONTRAINDICATIONS USING BIOCHEMICAL KNOWLEDGE GRAPHS
Various embodiments of the present disclosure disclose generating contraindication alert communications. A knowledge graph data structure, including a graph-based representation associated with a user identifier and having nodes and edges, is accessed. Edge weights are adjusted based on medical data associated with the user identifier. One or more sequential traversals of the knowledge graph data structure are performed until an equilibrium condition is met. Based on determining that a subset of nodes is associated with visit tallies totaling more than a threshold proportion of all node visits associated with the one or more sequential traversals, a contraindication alert communication, which includes representation of a biological effect for the user identifier, can be generated and transmitted.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
78.
Transfer learning techniques for using predictive diagnosis machine learning models to generate telehealth visit recommendation scores
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by an end-to-end machine learning framework that performs at least the following steps/operations: (i) a service request data object is processed by a diagnosis prediction machine learning model to generate a probabilistic diagnosis data object, (ii) the probabilistic diagnosis data object is processed by the hybrid diagnosis-provider classification machine learning model to generate a variable-length classification for the service request data object, and (iii) the variable-length classification is processed by a telehealth visit recommendation scoring machine learning model to generate a telehealth visit recommendation score for the service request data object.
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G16H 10/65 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
79.
MACHINE LEARNING TECHNIQUES FOR ENHANCED REDIRECTION RECOMMENDATION USING REAL-TIME ADJUSTMENT
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating predicted redirection scores based at least in part on: (i) generic redirection scores that are generated using provider evaluation machine learning models, and (ii) real-time redirection scores that are generated using real-time adjustment machine learning models.
Various embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using an agent machine learning model to determine an optimal clinical intervention based at least in part on the current clinical state and an inferred reinforcement learning policy that is determined based at least in part on a familiarity-adjusted reward function, where the familiarity-adjusted reward function is generated by an environment machine learning framework based at least in part on one or more next state predictions for one or more pruned action-state combinations based at least in part on a historical clinical outcome database, and the one or more pruned action-state combinations are determined based at least in part on one or more pruned clinical actions that are selected from a plurality of candidate clinical actions based at least in part on one or more action pruning criteria.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Educational services, namely, providing workshops, training, programs, and personal coaching in the fields of children's diets, nutrition, health, weight management, fitness and wellness Consulting to individuals and communities engaged in group weight loss programs; Counseling services in the fields of health, nutrition and lifestyle wellness; Health care services, namely, health and wellness programs in field of childhood obesity; Providing information about health, wellness and nutrition via a website
82.
MACHINE LEARNING TECHNIQUES FOR HYBRID TEMPORAL-UTILITY CLASSIFICATION DETERMINATIONS
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations by dynamically determining a hybrid temporal-utility classification for a predictive entity. The hybrid temporal-utility classification for the predictive entity may be determined based at least in part on outputs from a temporal score generation machine learning model and a utility score generation machine learning model.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by an end-to-end machine learning framework that performs at least the following steps/operations: (i) a service request data object is processed by a diagnosis prediction machine learning model to generate a probabilistic diagnosis data object, (ii) the probabilistic diagnosis data object is processed by the hybrid diagnosis-provider classification machine learning model to generate a variable-length classification for the service request data object, and (iii) the variable-length classification is processed by a recommendation scoring machine learning model to generate a consultation recommendation score for the service request data object.
Apparatus, systems, and methods for real time monitoring of a patient's breathing utilizing automatically controlled devices and machine learning based techniques to determine a full breath of a patient and to identify splinting points, and to thereby transmit stimulation signals to the patient so as to assist the patient breathe through splinting points. In some embodiments, a wearable breathing monitoring device comprising of one or more sensors configured to monitor the user's breathing and a stimulator apparatus comprising one or more transmitters configured to transmit stimulation signals to the patient at a time corresponding to a detected splinting point is provided. The stimulator apparatus is configured to apply electrical pulses according to a stimulation schedule via the transmitters to target nerves of the user's body.
Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for monitoring a user's movement in real-time and providing or augmenting stimulation. For example, various embodiments provide techniques generating movement prediction profiles using movement prediction machine learning models and for use in conjunction with wearable devices.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing optimized breathing therapy. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform optimized breathing therapy using at least one of interruption score generation machine learning models, observed inspiration-expiration pattern, expected inspiration-expiration patterns, expected musical patterns, and inferred musical patterns.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Providing temporary use of on-line non-downloadable software for tracking, reporting, reviewing and sharing health care data analytics Providing information in the fields of health and wellness; Providing a website featuring information about health, wellness and nutrition
90.
MACHINE LEARNING TECHNIQUES FOR PREDICTIVE RESPIRATORY QUALITY SCORE ASSIGNMENT
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive respiratory quality score assignment. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive respiratory quality score assignment using at least one of respiratory quality evaluation scoring machine learning models, explanation generation machine learning model, supplemental feature extraction machine learning model, and observed sensory data.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
36 - Financial, insurance and real estate services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Healthcare cost management services for health benefit plans of others; Health care administration services, namely, business administration of healthcare programs; Healthcare utilization and review services; Medical referrals in the nature of doctor and dentist referrals Health insurance underwriting; Health insurance administration services; Health insurance claim processing services; Health insurance brokerage services; Dental health insurance underwriting and administration; Vision health insurance underwriting and administration; Hearing health insurance underwriting and administration; Administration of employee benefit plans and prepaid healthcare plans; Insurance claims processing services in the field of dental, vision, and hearing health insurance; Life insurance underwriting; Life insurance administration services; Insurance services, namely, underwriting, issuance, and administration of supplemental health insurance, accident protection, critical illness protection, and hospital indemnity protection; Insurance services, namely, underwriting, issuance, and administration of short term disability, long term disability, and absence management insurance plans; Insurance claims processing services in the field of life, disability, absence management, accident protection, critical illness protection, and hospital indemnity protection insurance Providing health information; Providing information in the fields of dental health, vision health, and hearing health
92.
Dynamic delivery of modified user interaction electronic document data objects based at least in part on defined trigger events
There is a need to automatically provide one or more electronic documents to the user. In one example, embodiments comprise, generating a facility visit data object for a user describing one or more facility attributes for one or more facilities associated with a potential visit from the user. One or more electronic documents may be determined to be retrieved based at least in part on the facility visit data object. One or more user interaction electronic document data objects may be generated to enable interaction between the user and the one or more electronic document data objects. One or more modified user interaction electronic document data objects may be received and may be provided to one or more facility computing entities in response to one or more trigger events.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to categorical data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to categorical data objects by utilizing at least one of predictive feature hierarchies, feature refinement routines, decision subsets of predictive features that are generated based at least in part on predictiveness measures for the predictive features, and/or the like.
G06F 18/211 - Selection of the most significant subset of features
G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
G06F 18/245 - Classification techniques relating to the decision surface
G06F 18/27 - Regression, e.g. linear or logistic regression
94.
PREDICTIVE ANOMALY DETECTION USING DEFINED INTERACTION LEVEL ANOMALY SCORES
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive anomaly detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive anomaly detection by utilizing at least one of defined interaction level anomaly scores, such as defined interaction level anomaly scores for non-constant defined interaction levels that are determined using weighted feature tuple anomaly scores for feature tuple values that are associated with the non-constant defined interaction levels, as well as defined interaction level anomaly scores for constant defined interaction levels that are determined using an anomaly distribution measure for an anomaly quantization metric across a plurality of inferred predictive entities.
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Emergency medical services; Health care; Health counseling; Managed health care services; Providing information in the fields of health and wellness; Health care services, namely, wellness programs
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by dynamically parameterized machine learning frameworks, such as a dynamically parameterized machine learning framework comprising an encoder machine learning model that is configured to generate dynamically generated parameters for a target machine learning model of the dynamically parameterized machine learning framework.
Embodiments of the invention provide apparatuses, systems, and methods for more accurate remote monitoring of a user's body. In some embodiments, a system for monitoring a user's body comprises a wearable device, a video sensor attached at a collar portion of the wearable device, a plurality of audio sensors spaced and attached at a body portion of a wearable device and a controller configured to determine a Jugular Venous Pressure (JVP) of the user, and determine audio characteristics of an output of the plurality of audio sensors to generate an audio heat map corresponding to at least one internal organ of the user.
G06F 1/16 - Constructional details or arrangements
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
98.
Machine learning techniques for prospective event-based classification
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of prospective coverage score determination machine learning models and prospective event-based classification machine learning models.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
99.
Method, apparatus and computer program product for graph-based encoding of natural language data objects
Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include retrieving a plurality of natural language data objects from a database; determining, based at least in part on the plurality of natural language data objects and by utilizing an entity extraction machine learning model, a plurality of entity identifiers for the plurality of natural language data objects; determining, based at least in part on the plurality of entity identifiers and by utilizing the entity extraction machine learning model, one or more entity relationship identifiers for the plurality of natural language data objects; generating, based at least in part on the plurality of entity identifiers and the one or more entity relationship identifiers, a graph-based data object for the plurality of natural language data objects; and performing one or more prediction based actions based at least in part on the graph-based data object.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A controller for an artificial heart enables activity-specific adjustments to the operation of an artificial heart by obtaining sensor data from a plurality of sensors monitoring characteristics of a patient's body, and using the sensor data as input to one or more control parameter models for identifying control parameters to be provided to the artificial heart to adjust the operational parameters of the artificial heart. The controller is in wireless communication with the artificial heart via an application program interface (API)-based communication channel that facilitates communication between the controller and the artificial heart. Moreover, a cloud-based management computing entity may be utilized to train and/or execute one or more models to enable real-time updates to the operational characteristics of the artificial heart to enable the artificial heart to appropriately accommodate activities of the patient.
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
A61M 60/196 - Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient’s body replacing the entire heart, e.g. total artificial hearts [TAH]
A61M 60/531 - Regulation using real-time patient data using blood pressure data, e.g. from blood pressure sensors
A61M 60/892 - Active valves, i.e. actuated by an external force
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
