文献类型：期刊文献

英文题名：Knowledge-Routed Automatic Diagnosis With Heterogeneous Patient-Oriented Graph

作者：Li, Zhiang[1];Ruan, Tong[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：12

起止页码：89573

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20242516278537);WOS:【SCI-EXPANDED(收录号:WOS:001262677500001)】；

基金：This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Prospective Green Technology Innovation Project funded by the Korea Ministry of Environment (MOE) (No.2021003160004).

语种：英文

外文关键词：Automatic diagnosis; prior knowledge; heterogeneous patient-oriented graph; sequence generation; topological connection

摘要：Automated diagnosis, as a temporary medical supplement, has gained significant attention in research in recent years. Existing methods employ sequence generation approaches to inquire about symptoms and diagnose diseases. However, these methods ignore the fact that: 1) doctors utilize their past experience and similar cases to aid in diagnosis in real-world scenarios; 2) doctors inquire about key symptoms that serve as vital diagnostic evidence within limited conversations. To address these issues, we propose an end-to-end model KDPoG. Firstly, in addition to use the symptom and attribute embedding, we propose patient-oriented graph enhanced representation learning, which is built by a patient-oriented graph and learned with heterogeneous graph convolution networks. Furthermore, based on the encoder built with gated attention units, we propose knowledge-guided attention mechanism learning, which incorporates conditional probabilities of co-occurrence between symptom pairs. Finally, we utilize two linear layers as the classification module to achieve symptom probing and disease diagnosis. We conduct extensive experiments on four public datasets, which demonstrate that our proposed model outperforms the state-of-the-art methods. We achieve an average absolute improvement of over 2% in disease diagnosis accuracy. Particularly, on the Muzhi-10 dataset, we observe an absolute improvement of over 14.7% in symptom recall rate.