Systems and Methods for Selecting a Task-Specific Machine-Learning Model for Addressing a Clinical Task

1 . A method for selecting from among task-specific machine-learning models for addressing a clinical task, the method comprising:

receiving a prompt from a user; and

in accordance with determining that the prompt requests assistance with a clinical task:

selecting, by a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks, a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt;

providing the prompt to the respective task-specific machine-learning model that was selected from among the plurality of task-specific machine-learning models;

receiving a response to the prompt, wherein the response is generated by the respective task-specific machine-learning model; and

in accordance with determining that the response addresses the clinical task, providing the response to the user.

2 . The method of claim 1 , wherein the prompt comprises an identifier for a patient, an attribute of the patient, a test result of the patient, a diagnosis for the patient, or a combination thereof.

3 . The method of claim 1 , wherein the prompt is generated by the user by selecting and arranging graphical user interface elements within a user interface associated with the plurality of task-specific machine-learning models and/or the machine-learning model.

4 . The method of claim 1 , wherein the prompt comprises a plurality of text data comprising one or more text strings inputted by the user.

5 . The method of claim 1 , wherein the clinical task comprises:

(i) generating a summary report of a patient's medical records,

(ii) guiding a patient through a care plan,

(iii) creating patient care guidelines based on a patient's health profile,

(iii) identifying patients requiring follow-up at a hospital,

(v) identifying changes in a standard of care for a disease setting, or

(vi) evaluating unstructured data associated with a patient to identify a cohort of similar patients.

6 . The method of claim 1 , wherein the respective task-specific machine-learning model is selected from among the plurality of task-specific machine-learning models based on a divergence of a principal component analysis of the prompt for each respective task-specific machine learning model in the plurality of task-specific machine-learning models.

7 . The method of claim 1 , further comprising:

selecting at least two task-specific machine learning models from among the plurality of task-specific machine-learning models based on the prompt;

providing some or all of the prompt to each respective task-specific machine-learning model in the at least two task-specific machine learning models that was selected from among the plurality of task-specific machine-learning models; and

receiving respective information from each task-specific machine learning models in the at least two task-specific machine learning models, wherein the response corresponds to a combination of the respective information from at least two task-specific machine learning models.

8 . The method of claim 7 , further comprising:

selecting:

a first task-specific machine learning model in the at least two task-specific machine learning models as an initial terminal task-specific machine learning model, and

a second task-specific machine learning model in the at least two task-specific machine learning models as a final terminal task-specific machine learning model;

providing the prompt to the first task-specific machine-learning model;

receiving respective information from the first task-specific machine-learning model;

providing the respective information to the second task-specific machine-learning model; and

receiving the response to the prompt from the second task-specific machine-learning model, wherein the response was generated by the second task-specific machine-learning model.

9 . The method of claim 1 , wherein determining that the prompt requests assistance with a clinical task further comprises:

parsing the prompt into one or more commands, thereby forming an intent of the prompt for requesting assistance with a clinical task, and

identifying a first domain in a plurality of domains associated with the intent of the prompt.

10 . The method of claim 1 , wherein determining that the prompt requests assistance with a clinical task further comprises:

applying the prompt to a machine-learning model, thereby generating a first response different from the prompt and responsive to the prompt from the user;

obtaining a first domain in a plurality of domains of an input space associated with the prompt; and

evaluating a value of the first response, wherein

when the value of the first response satisfies a threshold condition, communicating, via a communication network, the first response to the user, and

when the value of the first response fails to satisfy the threshold condition,

identifying a first task-specific machine learning model associated with the first domain, and

applying the first response and/or the prompt to the first task-specific machine-learning model, thereby generating a second response different from the first response and responsive to the prompt.

11 . The method of claim 1 , wherein the respective task-specific machine-learning model is trained on a first domain in a plurality of domains.

12 . The method of claim 1 , wherein each respective domain in a plurality of domains comprises at least one task-specific machine-learning model trained on the respective domain.

13 . The method of claim 11 , wherein selecting the respective task-specific machine-learning model from among the plurality of task-specific machine-learning models is based on an identification of the first domain through an associated with the prompt.

14 . The method of claim 11 , wherein providing the prompt to the respective task-specific machine-learning model comprises:

applying the prompt to a first node in a plurality of interconnected nodes, thereby generating the response different from prompt and responsive to the prompt from the user, wherein

the first node is associated with a first domain-specific machine-learning model in the plurality of task-specific machine-learning models,

each task-specific machine-learning model in the plurality of task-specific machine-learning model (i) is associated with at least one nodes in the plurality of interconnected nodes and (ii) defines a conditional logic for performing a specific task, and

each node in the plurality of interconnected nodes is connected by an edge to at least one node in the plurality of interconnected nodes.

15 . The method of claim 1 , wherein selecting the respective task-specific machine-learning model comprises generating the task-specific machine-learning model having a conditional logic configured to respond to the prompt.

16 . The method of claim 1 , wherein selecting the respective task-specific machine-learning model comprises identifying a first classification of machine-learning models and selecting the respective the respective task-specific machine-learning model based on an association with the first classification of machine-learning models.

17 . The method of claim 1 , wherein selecting the respective task-specific machine learning model comprises forming a first order for a plurality of interconnected nodes.

18 . A computing system, comprising:

control circuitry;

memory; and

one or more sets of instructions stored in the memory and configured for execution by the control circuitry, the one or more sets of instructions comprising instructions for:

receiving a prompt from a user; and

in accordance with determining that the prompt requests assistance with a clinical task:

selecting, by a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks, a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt;

providing the prompt to the respective task-specific machine-learning model that was selected from among the plurality of task-specific machine-learning models;

receiving a response to the prompt, wherein the response is generated by the respective task-specific machine-learning model; and

in accordance with determining that the response addresses the clinical task, providing the response to the user.

19 . The computing system of claim 18 , wherein the respective task-specific machine-learning model is selected from among the plurality of task-specific machine-learning models based on a divergence of a principal component analysis of the prompt for each respective task-specific machine learning model in the plurality of task-specific machine-learning models.

20 . A non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory, the one or more sets of instructions comprising instructions for:

receiving a prompt from a user; and

in accordance with determining that the prompt requests assistance with a clinical task:

selecting, by a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks, a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt;

providing the prompt to the respective task-specific machine-learning model that was selected from among the plurality of task-specific machine-learning models;

receiving a response to the prompt, wherein the response is generated by the respective task-specific machine-learning model; and

in accordance with determining that the response addresses the clinical task, providing the response to the user.