@InProceedings{ling-EtAl:2017:I17-1,
  author    = {Ling, Yuan  and  Hasan, Sadid A.  and  Datla, Vivek  and  Qadir, Ashequl  and  Lee, Kathy  and  Liu, Joey  and  Farri, Oladimeji},
  title     = {Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {895--905},
  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.},
  url       = {http://www.aclweb.org/anthology/I17-1090}
}