Ensemble machine learning systems and methods
Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.
1 . A computer-based system comprising an ensemble of machine learning models, the computer-based system comprising:
at least one processor; and
at least one non-transitory memory having stored thereon instructions to configure the at least one processor to:
(a) carry out, or assist in carrying out, with a conversational machine learning model comprising a language model that is trained on a corpus of text, a conversation with a user employing at least one entropy minimization technique to generate a question to ask the user that minimizes diagnostic entropy;
(b) generate, with a diagnostic machine learning model, one or more of a differential diagnosis for the user and a diagnosis for the user based, at least in part, on the conversation; and
(c) carry out a task for the user, based on the diagnosis that is determined at least in part by the employing the entropy minimization technique, wherein the task comprises providing an actionable recommendation to the user, and wherein the actionable recommendation comprises a treatment for the diagnosis, a referral to a human healthcare provider, or a combination thereof;
wherein the conversational machine learning model and the diagnostic machine learning model form the ensemble of machine learning models.
2 . The computer-based system of claim 1 , wherein the computer-based system is configured to receive multimodal input from the user.
3 . The computer-based system of claim 2 , wherein the multimodal input comprises one or more of written input, spoken input, non-speech audio input, static image input, and video input.
4 . The computer-based system of claim 1 , wherein the conversation with the user includes at least one input from the user that is unstructured.
5 . The computer-based system of claim 4 , wherein natural language processing is applied to the at least one input from the user that is unstructured.
6 . The computer-based system of claim 1 , wherein the language model comprises a deep machine learning model.
7 . The computer-based system of claim 1 , wherein the conversational machine learning model is configured to minimize a number of questions needed to ask the user in the conversation to generate the one or more of the differential diagnosis and the diagnosis.
8 . The computer-based system of claim 1 , wherein the diagnostic machine learning model is at least partially trained using computer-generated medical data that is produced by a generative model.
9 . The computer-based system of claim 1 , wherein the healthcare task comprises connecting the user with a healthcare provider.
10 . The computer-based system of claim 1 , wherein the healthcare task comprises one or more of issuing a prescription, renewing a prescription, and providing a referral.
11 . The computer-based system of claim 1 , wherein the healthcare task comprises arranging testing for the user.
12 . The computer-based system of claim 1 , wherein the healthcare task comprises providing medical advice related to one or more of the conversation, the differential diagnosis, and the diagnosis.
13 . The computer-based system of claim 12 , wherein the medical advice that is provided is regulated by a regulatory authority.
14 . A computer-based method for utilizing an ensemble of machine learning algorithms to provide healthcare to a user, the method comprising:
carrying out, with a conversational machine learning model, comprising a language model that is trained on a corpus of text, a computer-generated conversation with the user employing at least one entropy minimization technique to generate a question to ask the user that minimizes diagnostic entropy;
applying a reasoning machine learning model to at least a portion of the conversation to determine a diagnosis; and
carry out a task for the user, based on the diagnosis that is determined at least in part by the employing the entropy minimization technique, wherein the task comprises providing an actionable recommendation to the user, and wherein the actionable recommendation comprises a treatment for the diagnosis, a referral to a human healthcare provider, or a combination thereof;
wherein the conversational machine learning model and the reasoning machine learning model form the ensemble of machine learning models.