A knowledge infused context driven dialogue agent for disease diagnosis using hierarchical reinforcement learning

Disease diagnosis is an essential and critical step in any disease treatment process. Automatic disease diagnosis has gained immense popularity in recent years, owing to its effiacy, easy accessability and reliablity. The major challenges for the diagnosis agent are inevitably large action space (symptoms) and varieties of diseases, which demand either rich domain knowledge or an intelligent learning framework. We propose a novel knowledge-infused context-driven (KI-CD) hierarchical reinforcement learning (HRL) based diagnosis dialogue system, which leverages a bayesian learning-inspired symptom investigation module called potential candidate module (PCM) for aiding context-aware, knowledge grounded symptom investigation. The PCM module serves as a context and knowledge guiding companion for lower-level policies, leveraging current context and disease-symptom knowledge to identify candidate diseases and potential symptoms, and reinforcing the agent for conducting an intelligent and context guided symptom investigation with the information enriched state and an additional critic known as learner critic. The knowledge-guided symptom investigation extracts an adequate set of symptoms for disease identification, whereas the context-aware symptom investigation aspect substantially improves topic (symptom) transition and enhances user experiences. Furthermore, we also propose and incorporate a hierarchical disease classifier (HDC) with the model for alleviating symptom state sparsity issues, which has led to a significant improvement in disease classification accuracy. The proposed framework outperforms the current state-of-the-art method on the multiple benchmarked datasets and, in all evaluation metrics other than dialogue length (diagnosis success rate, average match rate, symptom identification rate, and disease classification accuracy by 7.1 %, 0.23 %, 19.67 % and 8.04 %, respectively), which firmly establishes the effiacy of the proposed bayesian learning-inspired context-driven symptom investigation and disease diagnosis methodology (1). (C) 2022 Published by Elsevier B.V.

Automated summary

Disease diagnosis is a crucial step in the treatment process, and automatic disease diagnosis has gained popularity due to its efficiency, accessibility, and reliability. This study proposes a knowledge-infused context-driven hierarchical reinforcement learning diagnosis dialogue system, which utilizes a Bayesian learning-inspired symptom investigation module to aid context-aware and knowledge-grounded symptom investigation. The framework also incorporates a hierarchical disease classifier to alleviate symptom state sparsity issues.