Huaixiao Tou
2019
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph
Xinzhu Lin
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Xiahui He
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Qin Chen
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Huaixiao Tou
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Zhongyu Wei
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Ting Chen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.
2018
Task-oriented Dialogue System for Automatic Diagnosis
Zhongyu Wei
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Qianlong Liu
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Baolin Peng
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Huaixiao Tou
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Ting Chen
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Xuanjing Huang
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Kam-fai Wong
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Xiangying Dai
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
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.
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Co-authors
- Zhongyu Wei 2
- Ting Chen 2
- Xinzhu Lin 1
- Xiahui He 1
- Qin Chen 1
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