A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets

Chen, Wei; Li, Zhiwei; Hongyi, Fang,; Qianyuan, Yao,; Zhong, Cheng; Hao, Jianye; Zhang, Qi; Huang, Xuanjing; iajie, Peng, J; Wei, Zhongyu

doi:10.48550/arxiv.2204.08997

Cited by 1 publication

(1 citation statement)

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“…Symptom Attribute Symptoms are widely present in actual doctor-patient conversations, they are the main topics discussed in medical dialogues and important basis for doctors to make diagnosis (Zeng et al, 2020;Chen et al, 2022). However, symptoms alone in the dialogue are less informative, additional annotations are needed to find the relationship between symptoms and patients.…”

Section: Formalizationmentioning

confidence: 99%

DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations

Chen¹,

Zhong²,

Peng³

et al. 2022

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Motivation: Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Results: Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.

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Section: Formalizationmentioning

confidence: 99%