Systems and methods for responding to healthcare inquiries

Abstract

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 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.   
     
     
         2 . The method of  claim 1 , wherein the eliciting step comprises:
 using, by the conversational engine, an entropy minimization process to determine a next question to present to the user, such that subsequent information provided by the user in response to the next question minimizes the number of medical recommendations.   
     
     
         3 . The method of  claim 2 , wherein the entropy minimization process is weighted to optimize diagnosis that identifies worst outcomes for early medical intervention on the user. 
     
     
         4 . The method of  claim 2 , wherein the entropy minimization process is weighted to optimize diagnosis for treating the user's symptoms or disease clusters that maximizes a diagnostic value of the user's response to the proposed treatments. 
     
     
         5 . The method of  claim 1 , wherein the presenting step comprises invoking a knowledge base and a diagnosis engine. 
     
     
         6 . The method of  claim 5 , wherein the knowledge base represents normalized medical concepts, wherein the normalized medical concepts include at least one of:
 entities representing findings, symptoms, and conditions;   modifiers representing anatomical location, severity, and temporal modifiers;   weighted relations between the entities;   relations between the entities and the modifiers;   a representation of models and engines as an instance of a graph with the entities and the relations;   a mapping of a medical text to a knowledge base representation by using an entity recognition module; and   additional knowledge sources.   
     
     
         7 . The method of  claim 6 , wherein the entity recognition component module is configured to translate health-related text into medical entities and modifiers. 
     
     
         8 . The method of  claim 5 , wherein the diagnosis engine is at least one of:
 a first diagnosis engine based on rules in a knowledge based codifying probabilistic relationships between symptoms/findings and diseases;   a second diagnosis engine based on first machine-learned models deriving relations between symptoms/findings and diseases from historical medical records;   a third diagnosis engine based on second machine-learned models deriving both probabilities and relationships from historic medical records; and   a fourth diagnosis engine based on third machine-learned models learned from mixed data that includes at least one of synthetic data generated by a pre-existing expert system, electronic medical records, manual cases, labeled cases from the diagnosis engine.   
     
     
         9 . The method of  claim 8 , wherein the historical records are either obtained from anonymized databases or generated by interactions with the user in a recursive manner. 
     
     
         10 . The method of  claim 8 , wherein
 the first, second, third, and fourth diagnosis engines act in ensemble;   each of the first, second, third, and fourth diagnosis engines operates upon a current state of knowledge independently, and offers possible responses along with a confidence and a value estimate; and   an ensemble arbitrator chooses a response out of the possible responses or a collection of responses that is best for the user or circumstance given a match or a mismatch between the possible responses, and the value and the confidence estimate each of the first, second, third, and fourth diagnosis engines expresses in its corresponding response, wherein
 the ensemble arbitrator learns a weight to use for each possible response from each of the first, second, third, and fourth diagnosis engines based upon history. 
   
     
     
         11 . The method of  claim 1 , wherein the eliciting step includes invoking a natural language understanding engine. 
     
     
         12 . The method of  claim 1 , wherein the information is in the form of at least one of text, speech, imagery, sound, and medical test results. 
     
     
         13 . The method of  claim 1 , wherein the information from the user includes at least one of user's medical history and the user's symptoms. 
     
     
         14 . The method of  claim 1 , wherein the conversational engine is one of a diagnosis conversational engine, an information conversational engine, a referral conversational engine, and a treatment conversational engine. 
     
     
         15 . The method of  claim 1 , further comprising, prior to presenting the one or more medical recommendations to the user, seeking approval or revision of the one or more medical recommendations by a medical expert. 
     
     
         16 . At least one processor readable storage medium storing a computer program of instructions configured to be readable by at least one processor for instructing the at least one processor to execute a computer process for performing the method as recited in  claim 1 . 
     
     
         17 . A system for responding to a healthcare inquiry from a user, the system comprising:
 memory for storing instructions; and   a processor configured to execute the instructions to:
 classify an intent of the user based on the healthcare inquiry; 
 instantiate a conversational engine based on the intent; 
 elicit, by the conversational engine, information from the user; and 
 present one or more medical recommendations to the user based at least in part on the information.