Learning from Failure: Towards Developing a Disease Diagnosis Assistant That Also Learns from Unsuccessful Diagnoses

In recent years, automatic disease diagnosis has gained immense popularity in research and industry communities. Humans learn a task through both successful and unsuccessful attempts in real life, and physicians are not different. When doctors fail to diagnose disease correctly, they re-assess the extracted symptoms and re-diagnose the patient by inspecting a few more symptoms guided by their previous experience and current context. Motivated by the experience gained from failure assessment, we propose a novel end-to-end automatic disease diagnosis dialogue system called Failure Assessment incorporated Symptom Investigation and Disease Diagnosis (FA-SIDD) Assistant. The proposed FA-SIDD model includes a knowledge-guided, incorrect disease projection-aware failure assessment module that analyzes unsuccessful diagnosis attempts and reinforces the assessment for further investigation and re-diagnosis. We formulate a novel Markov decision process for the proposed failure assessment, incorporating symptom investigation and disease diagnosis frameworks, and optimize the policy using deep reinforcement learning. The proposed model has outperformed several baselines and the existing symptom investigation and diagnosis methods by a significant margin (1–3%) in all evaluation metrics (including human evaluation). The improvements over the multiple datasets and across multiple algorithms firmly establish the efficacy of learning gained from unsuccessful diagnoses. The work is the first attempt that investigate the importance of learning gained from unsuccessful diagnoses. The developed assistant learns diagnosis task more efficiently than traditional assistants and shows robust behavior. Furthermore, the code is available at https://github.com/AbhisekTiwari/FA-SIDA.