Interactive conversational symptom checker

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform:
• fine-tuning a clinical BERT model, using a cross-entropy loss function, to recognize symptom entities related to symptoms;
• receiving text-based evidence from a user, wherein the text-based evidence comprises one or more of the symptoms;
• translating the text-based evidence received from the user into one or more codes for the one or more of the symptoms, wherein the translating comprises:
• recognizing one or more of the symptom entities related to the one or more of the symptoms in the text-based evidence, using the fine-tuned, clinical BERT model;
• and
• mapping the one or more of the symptom entities to the one or more codes;
• generating, from an undirected graph, a subgraph mapping the one or more of the symptoms to connected diseases, wherein nodes of the undirected graph comprise diseases and symptoms;
• receiving a history of conversation between the user and a question generator;
• calculating (a) a respective posterior probability for each of the connected diseases, using the subgraph, and (b) a hypothesis diagnosis based on the text-based evidence;
• and
• iteratively and until a predetermined number of iterations are conducted:
• determining one or more questions to send to the user based on a set of top symptoms for the hypothesis diagnosis;
• sending the one or more questions to the user;
• receiving one or more responses from the user in response to the one or more questions;
• and
• updating the hypothesis diagnosis based on the one or more responses and on the history of conversation between the user and the question generator;
• and
• sending a recommendation to the user to schedule an appointment with a provider for the updated hypothesis diagnosis, as updated.