Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Yuan Ling, Sadid A. Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, Oladimeji Farri
Abstract
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.- Anthology ID:
- I17-1090
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 895–905
- Language:
- URL:
- https://aclanthology.org/I17-1090
- DOI:
- Cite (ACL):
- Yuan Ling, Sadid A. Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, and Oladimeji Farri. 2017. Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 895–905, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning (Ling et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/I17-1090.pdf