迈向知识注入的自动化疾病诊断助手
Towards Knowledge-Infused Automated Disease Diagnosis Assistant
AI总结：
本文提出知识注入、话语感知的双通道疾病诊断模型 KI-DDI，并构建共情医患对话语料库，证明额外症状提取与医学知识融合可显著提升诊断效果。
AI中文摘要：
随着互联网通信和远程医疗的发展，人们越来越多地转向网络开展各类医疗健康活动。随着疾病和症状数量不断增加，诊断患者变得具有挑战性。在本工作中，我们构建了一个诊断助手来协助医生，该助手基于医患互动识别疾病。在诊断过程中，医生同时运用症状学知识和诊断经验，以准确且高效地识别疾病。受此启发，我们通过医患互动研究医学知识在疾病诊断中的作用。我们提出了一种双通道、知识注入且具有话语感知能力的疾病诊断模型（KI-DDI）：第一个通道使用基于 transformer 的编码器对医患沟通进行编码，另一个通道则使用图注意力网络（GAT）创建症状—疾病嵌入。在下一阶段，对话嵌入与知识图谱嵌入被融合，并输入深度神经网络以进行疾病识别。此外，我们首先开发了一个共情式对话医学语料库，其中包含患者与医生之间的对话，并标注有意图和症状信息。所提出的模型相较现有最先进模型表现出显著提升，确立了以下两点的关键作用：(a) 医生为额外症状提取所做的努力（除患者自我报告之外），以及 (b) 注入医学知识以有效识别疾病。很多时候，患者还会展示其医学状况，这成为诊断中的关键证据。因此，整合视觉感官信息将是增强诊断助手能力的一条有效途径。
英文摘要：
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.
