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AI Biotech/Diagnostics: Cardio
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Application US20190156944
Published 2019-05-23
Utilizing A Machine Learning Model To Identify Activities And Deviations From The Activities By An Individual
A device receives historical information associated with an individual to be monitored, wherein the historical information includes at least one of information associated with a health history of the individual, health histories of other individuals, activities of the individual, or activities of the other individuals. The device receives monitored information associated with the individual from one or more client devices associated with the individual, and pre-processes the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model. The device processes the pre-processed monitored information, with a trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual, and performs one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities.
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- 1. A method, comprising:
receiving, by a device, configuration information associated with configuring an application for monitoring an individual, wherein the configuration information includes at least one of:
information identifying physical characteristics of the individual,
information identifying medications taken by the individual,
personal information of the individual, or
information associated with a caregiver of the individual;
receiving, by the device, historical information associated with the individual,
wherein the historical information includes at least one of:
information associated with a health history of the individual,
information associated with health histories of other individuals,
information associated with activities of the individual, or
information associated with activities of the other individuals;
training, by the device and based on the configuration information and the historical information, a machine learning model to generate a trained machine learning model; receiving, by the device and via the application, monitored information associated with the individual from one or more client devices associated with the individual; processing, by the device, the monitored information, with the trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual; and performing, by the device, one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities by the individual.
- 8. A device, comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories, to:
receive historical information associated with an individual to be monitored,
wherein the historical information includes at least one of:
information associated with a health history of the individual,
information associated with health histories of other individuals,
information associated with activities of the individual, or
information associated with activities of the other individuals;
receive monitored information associated with the individual from one or more client devices associated with the individual; pre-process the monitored information to generate pre-processed monitored information that is understood by a trained machine learning model; process the pre-processed monitored information, with the trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual; and perform one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities by the individual.
- 15. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:
provide an application for monitoring an individual to a client device associated with a caregiver of the individual;
receive a machine learning model that has been trained based on configuration information and historical information,
wherein the configuration information is associated with configuring the application and includes at least one of:
information identifying physical characteristics of the individual,
information identifying medications taken by the individual,
personal information of the individual, or
information associated with the caregiver of the individual;
wherein the historical information includes at least one of:
information associated with a health history of the individual,
information associated with health histories of other individuals,
information associated with activities of the individual, or
information associated with activities of the other individuals;
provide third-party application programming interfaces (APIs) to one or more client devices associated with the individual;
receive, via the application and the third-party APIs, monitored information associated with the individual from the client device associated with the caregiver and/or the one or more client devices associated with the individual;
process the monitored information, with the machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual; and
perform one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities by the individual.
