Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Ling Yuan, Sadid A. Hasan, Vivek V. Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri · International Joint Conference on Natural Language Processing · 2017
Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating clinical diagnosis as a reinforcement learning problem. During training, the reinforcement learning agent mimics the clinician’s cognitive process and learns the optimal policy to obtain the most appropriate diagnoses for a clinical narrative. This is achieved through an iterative search for candidate diagnoses from external knowledge sources via a sentence-by-sentence analysis of the inherent clinical context. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses. Experiments on the TREC CDS datasets demonstrate the effectiveness of our system over various non-reinforcement learning-based systems.