Hierarchical Reinforcement Learning for Automatic Disease Diagnosis

TL;DR

This paper introduces a hierarchical reinforcement learning framework for disease diagnosis dialogue systems, significantly improving accuracy and symptom recall over existing flat-policy models in complex scenarios.

Contribution

The paper proposes a novel hierarchical policy structure for disease diagnosis dialogue systems, enabling better handling of large symptom and disease spaces compared to prior flat-policy approaches.

Findings

Higher diagnosis accuracy on real-world datasets

Improved symptom recall in diagnosis process

Established a benchmark for future research

Abstract

Motivation: Disease diagnosis oriented dialogue system models the interactive consultation procedure as Markov Decision Process and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in the simple scenario when the action space is small, however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The high-level policy consists of amastermodel that is responsible for triggering a low-levelmodel, the lowlevel policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms. Results: Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches. Availability: The code and data is available from https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis Contact: 21210980124@m.fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.