A method may receive historical metabolic values for an individual having a first medical condition. A method may provide a first subset of the historical metabolic values to a machine learning model to train a generative machine learning model. A method may generate a first predicted metabolic value based on the first subset of historical metabolic values. A method may calculate a root mean square error (RMSE) between the first predicted metabolic value and a corresponding actual metabolic value of a second subset of historical metabolic values. A method may train the generative machine learning model to minimize the RMSE. A method may generate a trained generative machine learning model based on the 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
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
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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 50/70 - 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
2.
SYSTEMS AND METHODS FOR CONTINUOUS GLUCOSE MONITORING OUTCOME PREDICTIONS
Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.
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 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
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
Methods and devices include managing a metabolic metric of a user by determining an activity behavior score for the user based on self-monitoring activity inputs and activity performance. A carbohydrate behavior score for the user may be determined based on self-monitoring carbohydrate inputs and carbohydrate performance. A medicine behavior score may be determined based on user consumption of medicine according to a medicine schedule. A user cluster may be identified from a plurality of clusters that are determined based on receiving initial user activity, carbohydrate and medicine behavior scores for a plurality of initial users. The plurality of user clusters may be generated by applying a clustering algorithm to the initial user activity, carbohydrate, medicine behavior scores. A metabolic metric trend may be determined based on the user cluster and used to generate a treatment plan for the user to improve a metabolic metric outcome.
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
4.
SYSTEMS AND METHODS FOR ANALYZING, INTERPRETING, AND ACTING ON CONTINUOUS GLUCOSE MONITORING DATA
Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing selfmanagement behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.
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 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 50/30 - 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
Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.
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 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
6.
DATABASE MANAGEMENT AND GRAPHICAL USER INTERFACES FOR MEASUREMENTS COLLECTED BY ANALYZING BLOOD
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
7.
ADAPTIVE ANALYTICAL BEHAVIORAL AND HEALTH ASSISTANT SYSTEM AND RELATED METHOD OF USE
This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.
patents
