Machine learning models for generating executable sequences
A system includes processor hardware configured to execute instructions from memory hardware. The instructions include, in response to designation of an entity within a data store, obtaining information and determining a condition of the designated entity. The instructions include, based on the condition of the designated entity, identifying a set of states. The instructions include obtaining trigger conditions for the selected state, each specifying a set of satisfaction criteria. The instructions include determining whether each trigger condition is satisfied by evaluating the satisfaction criteria based on data corresponding to the designated entity. The instructions include selectively selecting another state based on whether the trigger conditions are satisfied. The instructions include determining, and scheduling for execution, an executable sequence based on the selected state.
1 . A system comprising:
memory hardware configured to store instructions and a data store; and
processor hardware configured to execute the instructions, wherein the instructions include:
in response to designation of an entity within the data store:
obtaining information corresponding to the designated entity from the data store, wherein the obtained information includes at least one of prescription information, medical information, exercise information, nutrition information, or lab information;
obtaining a plurality of responses, wherein the plurality of responses is associated with a plurality of evaluation points;
based on a set of frequencies associated with the plurality of responses:
retraining a first machine learning model, and
modifying the plurality of evaluation points using the retrained first machine learning model; and
determining, via a second machine learning model, a health status of the designated entity based on the obtained information and the plurality of responses;
based on the health status of the designated entity, identifying a set of states and selecting a state from the set of states;
obtaining a set of trigger conditions for the selected state, wherein each trigger condition of the set of trigger conditions specifies a set of satisfaction criteria;
for each trigger condition of the set of trigger conditions, determining whether the trigger condition is satisfied by evaluating the set of satisfaction criteria of the trigger condition based on data corresponding to the designated entity;
selectively selecting another state from the set of states based on whether the trigger conditions of the set of trigger conditions are satisfied;
determining an executable sequence based on the selected state; and
scheduling the executable sequence for execution.
2 . The system of claim 1 wherein the selecting another state is performed in response to none of the set of trigger conditions being satisfied.
3 . The system of claim 1 wherein the selecting another state is performed in response to at least one of the set of trigger conditions failing to be satisfied.
4 . The system of claim 1 wherein the selecting another state is performed in response to a defined profile of the set of trigger conditions failing to be satisfied.
5 . The system of claim 1 wherein:
verification is selectively specified for a trigger condition of the set of trigger conditions; and
in response to verification being specified for the trigger condition, the trigger condition is determined to be satisfied only in response to successful verification of the satisfaction criteria of the trigger condition.
6 . The system of claim 5 wherein the successful verification requires that the set of satisfaction criteria of the trigger condition be satisfied multiple times.
7 . The system of claim 6 wherein the successful verification requires that the set of satisfaction criteria of the trigger condition be satisfied multiple times in greater than a specified time window.
8 . The system of claim 7 wherein the specified time window is 24 hours.
9 . The system of claim 1 wherein the executable sequence selectively includes transmitting a message soliciting data acquisition to a person corresponding to the designated entity.
10 . The system of claim 9 wherein the transmitting specifies a communications channel selected from a plurality of specified communication channels.
11 . The system of claim 9 wherein the transmitting includes transmitting a message to the person via a data acquisition device located at a residence of the person.
12 . The system of claim 1 wherein the determining the executable sequence includes at least one of:
selecting the executable sequence from a data structure storing a plurality of executable sequences; or
generating the executable sequence.
13 . The system of claim 12 wherein the generating the executable sequence includes incorporating sequence elements based on input from a clinician user interface.
14 . The system of claim 1 wherein the executable sequence selectively includes scheduling a point-to-point communication with a person corresponding to the designated entity.
15 . The system of claim 1 wherein the data corresponding to the designated entity is based on electronic health records.
16 . The system of claim 1 wherein the data store includes a relational database.
17 . The system of claim 1 wherein:
each state of the set of states is associated with a priority; and
the selecting initially selects a highest priority one of the set of states.
18 . The system of claim 17 wherein:
the states include an escalation state, an intervention state, and a normal state;
a priority of the escalation state is higher than a priority of the intervention state; and
the priority of the intervention state is higher than a priority of the normal state.
19 . A computerized method comprising:
in response to designation of an entity within a data store:
obtaining information corresponding to the designated entity from the data store, wherein the obtained information includes at least one of prescription information, medical information, exercise information, nutrition information, or lab information;
obtaining a plurality of responses, wherein the plurality of responses is associated with a plurality of evaluation points;
based on a set of frequencies associated with the plurality of responses:
retraining a first machine learning model, and
modifying the plurality of evaluation points using the retrained first machine learning model; and
determining, via a second machine learning model, a health status of the designated entity based on the obtained information and the plurality of responses;
based on the health status of the designated entity, identifying a set of states and selecting a state from the set of states;
obtaining a set of trigger conditions for the selected state, wherein each trigger condition of the set of trigger conditions specifies a set of satisfaction criteria;
for each trigger condition of the set of trigger conditions, determining whether the trigger condition is satisfied by evaluating the satisfaction criteria of the trigger condition based on data corresponding to the designated entity;
selectively selecting another state from the set of states based on whether the trigger conditions of the set of trigger conditions are satisfied;
determining an executable sequence based on the selected state; and
scheduling the executable sequence for execution.
20 . A non-transitory computer-readable medium comprising processor-executable instructions, wherein the instructions include:
in response to designation of an entity within a data store:
obtaining information corresponding to the designated entity from the data store, wherein the obtained information includes at least one of prescription information, medical information, exercise information, nutrition information, or lab information;
obtaining a plurality of responses, wherein the plurality of responses is associated with a plurality of evaluation points;
based on a set of frequencies associated with the plurality of responses:
retraining a first machine learning model, and
modifying the plurality of evaluation points using the retrained first machine learning model; and
determining, via a second machine learning model, a health status of the designated entity based on the obtained information and the plurality of responses;
based on the health status of the designated entity, identifying a set of states and selecting a state from the set of states;
obtaining a set of trigger conditions for the selected state, wherein each trigger condition of the set of trigger conditions specifies a set of satisfaction criteria;
for each trigger condition of the set of trigger conditions, determining whether the trigger condition is satisfied by evaluating the satisfaction criteria of the trigger condition based on data corresponding to the designated entity;
selectively selecting another state from the set of states based on whether the trigger conditions of the set of trigger conditions are satisfied;
determining an executable sequence based on the selected state; and
scheduling the executable sequence for execution.