Task-oriented Dialogue System for Automatic Diagnosis

In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients’ self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[2]  Bo Zhang,et al.  Automatic infection detection based on electronic medical records , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[3]  Finale Doshi-Velez,et al.  Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis , 2014, Pediatrics.

[4]  Siddhartha R. Jonnalagadda,et al.  Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials , 2017, Journal of Cardiovascular Translational Research.

[5]  Steve J. Young,et al.  A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies , 2006, The Knowledge Engineering Review.

[6]  Milica Gasic,et al.  POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.

[7]  Ya Zhang,et al.  A machine learning-based framework to identify type 2 diabetes through electronic health records , 2017, Int. J. Medical Informatics.

[8]  David Milward,et al.  Ontology-Based Dialogue Systems , 2003 .

[9]  Jianfeng Gao,et al.  End-to-End Task-Completion Neural Dialogue Systems , 2017, IJCNLP.

[10]  Edward Y. Chang,et al.  Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning , 2018, AAAI.

[11]  Jimeng Sun,et al.  Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..

[12]  Kai-Fu Tang,et al.  Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning , 2016 .

[13]  Kam-Fai Wong,et al.  Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning , 2017, EMNLP.

[14]  Stephen B. Johnson,et al.  A review of approaches to identifying patient phenotype cohorts using electronic health records , 2013, J. Am. Medical Informatics Assoc..

[15]  Benjamin S. Glicksberg,et al.  Identification of type 2 diabetes subgroups through topological analysis of patient similarity , 2015, Science Translational Medicine.

[16]  Kam-Fai Wong,et al.  Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Hui Ye,et al.  Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System , 2007, NAACL.

[18]  Oliver Lemon,et al.  Strategic Dialogue Management via Deep Reinforcement Learning , 2015, NIPS 2015.

[19]  Zhoujun Li,et al.  Building Task-Oriented Dialogue Systems for Online Shopping , 2017, AAAI.

[20]  David Vandyke,et al.  A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.