Methods and systems for implementing personalized health application
A method for generating a graphical user interface (GUI) can include receiving a first user request to log in to a session of an application. The method can further include obtaining first user characteristic information, including one or more user psychological information, user environmental information, or user lifestyle information. The method can further include selecting a first set of GUI elements to be presented to the user during the session, wherein the first set of GUI elements are selected based on the first user characteristic information. The method can further include generating a GUI that includes the first set of GUI elements and transmitting the GUI to a user device for display during the session.
1 . A system comprising:
one or more processors;
memory coupled to the one or more processors; and
instructions stored in the memory executed by the one or more processors, the instructions including instructions to:
receive caregiver training data;
provide the caregiver training data to a machine learning training module;
store output of the machine learning module as a model, wherein the machine learning module output is based, at least in part, on the caregiver training data;
receive caregiver input data associated with a particular caregiver;
provide the caregiver input data to a machine learning evaluation module;
provide the model to the machine learning evaluation module;
receive caregiver evaluation output from the machine learning evaluation module, wherein the caregiver evaluation output is generated based, at least in part, on the model and the caregiver input data;
store the caregiver evaluation output in a caregiver profile;
select a caregiving solution based, at least in part, on the caregiver evaluation output; and
provide, to a user, information associated with the caregiving solution.
2 . The system of claim 1 , wherein the machine learning training module comprises instructions to:
perform first comparisons of individual information consumption preferences of individual caregivers to caregiver characteristics associated with the individual caregivers;
establish first links between caregiver characteristics and information consumption preferences based on the first comparisons;
perform second comparisons of sets of information consumption preferences of the individual caregivers to sets of caregiver characteristics associated with individual caregivers; and
establish second links between sets of caregiver characteristics information and consumption preferences based on the first comparisons.
3 . The system of claim 2 , wherein the caregiver training data comprises individual information consumption preferences of individual caregivers and caregiver characteristics associated with the individual caregivers.
4 . The system of claim 2 , wherein the machine learning evaluation module comprises instructions to:
receive the caregiver input data;
evaluate the caregiver input data to identify information consumption preferences of the particular caregiver; and
establish one or more links between the particular caregiver and one or more items of information conforming to the information consumption preferences of the particular caregiver.
5 . The system of claim 4 , wherein the caregiver input data comprises caregiver characteristics of the particular caregiver.
6 . The system of claim 4 , wherein the instructions further comprise instructions to update the first links and the second links based, at least in part, on the one or more links between the particular caregiver and the one or more items of information.
7 . The system of claim 1 wherein the caregiver training data comprises information indicating consumption preferences associated with caregivers and caregiver characteristics of the caregivers.
8 . The system of claim 1 wherein the model comprises a weighted links table comprising link values determined based on correspondences among the caregiver training data.
9 . The system of claim 1 wherein the caregiver evaluation output comprises links to caregiver information preferences.
10 . A method for operating a health-related application, the method comprising:
receiving caregiver training data;
providing the caregiver training data to a machine learning training module;
storing output of the machine learning module as a model, wherein the machine learning module output is based, at least in part, on the caregiver training data;
receiving caregiver input data associated with a particular caregiver;
providing the caregiver input data to a machine learning evaluation module;
providing the model to the machine learning evaluation module;
receiving caregiver evaluation output from the machine learning evaluation module, wherein the caregiver evaluation output is generated based, at least in part, on the model and the caregiver input data;
storing the caregiver evaluation output in a caregiver profile;
selecting a caregiving solution based, at least in part, on the caregiver evaluation output; and
providing, to a user, information associated with the caregiving solution.
11 . The method of claim 10 , further comprising:
performing, by the machine learning training module, first comparisons of individual information consumption preferences of individual caregivers to caregiver characteristics associated with the individual caregivers;
establishing, by the machine learning training module, first links between caregiver characteristics and information consumption preferences based on the first comparisons;
performing, by the machine learning training module, second comparisons of sets of information consumption preferences of the individual caregivers to sets of caregiver characteristics associated with individual caregivers; and
establishing, by the machine learning training module second links between sets of caregiver characteristics information and consumption preferences based on the first comparisons.
12 . The method of claim 11 , wherein the caregiver training data comprises individual information consumption preferences of individual caregivers and caregiver characteristics associated with the individual caregivers.
13 . The method of claim 10 , further comprising:
receiving, by the machine learning evaluation module, the caregiver input data;
evaluating, by the machine learning evaluation module, the caregiver input data to identify information consumption preferences of the particular caregiver; and
establishing, by the machine learning evaluation module, one or more links between the particular caregiver and one or more items of information conforming to the information consumption preferences of the particular caregiver.
14 . The method of claim 13 , wherein the caregiver input data comprises caregiver characteristics of the particular caregiver.
15 . The method of claim 13 , further comprising updating the first links and the second links based, at least in part, on the one or more links between the particular caregiver and the one or more items of information.
16 . One or more computer readable storage mediums having instructions stored thereon which, when executed by a processor, cause the processor to:
receive caregiver training data;
provide the caregiver training data to a machine learning training module;
store output of the machine learning module as a model, wherein the machine learning module output is based, at least in part, on the caregiver training data;
receive caregiver input data associated with a particular caregiver;
provide the caregiver input data to a machine learning evaluation module;
provide the model to the machine learning evaluation module;
receive caregiver evaluation output from the machine learning evaluation module, wherein the caregiver evaluation output is generated based, at least in part, on the model and the caregiver input data;
store the caregiver evaluation output in a caregiver profile;
select a caregiving solution based, at least in part, on the caregiver evaluation output; and
provide, to a user, information associated with the caregiving solution.
17 . The one or more computer readable storage mediums of claim 16 , wherein the instructions further comprise instructions to:
perform, by the machine learning training module, first comparisons of individual information consumption preferences of individual caregivers to caregiver characteristics associated with the individual caregivers;
establish, by the machine learning training module, first links between caregiver characteristics and information consumption preferences based on the first comparisons;
perform, by the machine learning training module, second comparisons of sets of information consumption preferences of the individual caregivers to sets of caregiver characteristics associated with individual caregivers; and
establish, by the machine learning training module, second links between sets of caregiver characteristics information and consumption preferences based on the first comparisons.
18 . The one or more computer readable storage mediums of claim 16 , wherein the instructions further comprise instructions to:
receive the caregiver input data;
evaluate the caregiver input data to identify information consumption preferences of the particular caregiver; and
establish one or more links between the particular caregiver and one or more items of information conforming to the information consumption preferences of the particular caregiver.
19 . The one or more computer readable storage mediums of claim 16 , wherein the instructions further comprise instructions to update the first links and the second links based, at least in part, on the one or more links between the particular caregiver and the one or more items of information.
20 . The one or more computer readable storage mediums of claim 16 , wherein the model comprises a weighted links table comprising link values determined based on correspondences among the caregiver training data.