Task-oriented Dialogue System for Automatic Diagnosis

By Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-Fai Wong, Xiangying Dai. 2018

245 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Tat-seng Chua
3. Fuli Feng
4. Xiang Wang
5. Hanwang Zhang
6. Chao Yu
7. Jiming Liu
8. Shamim Nemati

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “in. The SC system was successfully employed in the DeepQ Tricorder which won the second prize in the Qualcomm Tricorder XPRIZE competition in year 2017 [275], [276]. A dialogue system was proposed in [277] for automatic diagnosis, in which the medical dataset was built from a pediatric department in”
• HuatuoGPT, towards Taming Language Model to Be a Doctor (2023, background): “• This is among the first work that conducts systematic evaluation in medical LLMs. Training language models from purely real-world conversation was a common practice [5–8].”
• Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022, background): “…and symbolic reasoning ( e.g., addition, sorting, or counting) (Dua et al., 2019) and enables the comprehension of complex contexts and compositional questions, which is critical for many practical applications such as automatic diagnosis (Wei et al., 2018) and robo-advisor (Fisch et al., 2019).”