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    Generating Instructions for Physical Experiments Using Whole Body Digital Twin Technology
    2.
    发明申请
    Generating Instructions for Physical Experiments Using Whole Body Digital Twin Technology 有权

    公开(公告)号：US20230110674A1

    公开(公告)日：2023-04-13

    申请号：US17963933

    申请日：2022-10-11

    申请人： Twin Health, Inc.

    发明人： James Wilson ,  Abhik Banerjee ,  Terrence Chun Yin Poon ,  Jahangir Mohammed

    IPC分类号： G16H10/20

    摘要： A Digital Twin clinical trial simulator determines one or more trial parameters for a physical experiment to validate a candidate treatment recommendation. For each of one or more variations of the physical experiment, the Digital Twin clinical trial simulator determines an effectiveness of the variation in validating the candidate treatment recommendation. The effectiveness describes a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state. For a variation that satisfies a threshold effectiveness, the Digital Twin clinical trial simulator determines one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation. The Digital Twin clinical trial simulator generates instructions for a medical professional to perform a physical experiment by adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features.

    PRECISION TREATMENT WITH MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY FOR OPTIMAL METABOLIC OUTCOMES
    7.
    发明申请
    PRECISION TREATMENT WITH MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY FOR OPTIMAL METABOLIC OUTCOMES 有权

    公开(公告)号：US20210045694A1

    公开(公告)日：2021-02-18

    申请号：US16993184

    申请日：2020-08-13

    申请人： TWIN HEALTH, INC.

    发明人： FREDERICK HADLEY ,  THOMAS ALAN DUNLAP ,  TERRENCE CHUN YIN POON

    IPC分类号： A61B5/00 ,  A61B5/145 ,  G16H50/50 ,  G16H40/67 ,  G16H10/40 ,  G16H50/30 ,  G16H20/60 ,  G16H10/60 ,  G06N20/00

    摘要： A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform encodes the biosignals into a vector representation and inputs the vector representation into a patient-specific metabolic model to determine a metabolic state of the patient at a conclusion of the current time period. The patient-specific metabolic model comprises a set of parameter values determined based on labels assigned to the previous metabolic states and a function representing one or more effects of the plurality of biosignals of the personalized metabolic profile. The platform compares the determined metabolic state of the patient to a threshold metabolic state representing a target metabolism. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve the determined metabolic state to the functional metabolic state.

    DYNAMICALLY MODELING THE EFFECT OF FOOD ITEMS AND ACTIVITY ON A PATIENT'S METABOLIC HEALTH
    8.
    发明公开
    DYNAMICALLY MODELING THE EFFECT OF FOOD ITEMS AND ACTIVITY ON A PATIENT'S METABOLIC HEALTH 审中-公开

    公开(公告)号：US20240331839A1

    公开(公告)日：2024-10-03

    申请号：US18623790

    申请日：2024-04-01

    申请人： Twin Health, Inc.

    发明人： Javad SOVIZI ,  James WILSON ,  Frederick HADLEY ,  Christopher SZETO ,  Terrence Chun Yin POON

    IPC分类号： G16H20/60 ,  G16H40/67 ,  G16H50/20 ,  G16H50/70

    CPC分类号： G16H20/60 ,  G16H40/67 ,  G16H50/20 ,  G16H50/70

    摘要： Disclosed herein is a method, system, and computer-readable medium for recommending foods to a patient. The disclosure includes accessing a record of food items recorded by a patient, including a classification of each food item. The method retrieves a current metabolic profile of the patient. Using a machine learning model, the method determines an updated classification for the food items and generates a notification for the patient. Additionally disclosed is a method, system, and computer-readable medium for recommending activities and activity times to a patient. The method includes accessing a record of activities previously recorded by a patient, each entry in the recordings including a duration of the activity and biosignal measurements. The method determines an effect of each activity on the metabolic state of the patient using a machine learning model. The method identifies activities that improve the patient's metabolic state and generates a recommendation for the patient.