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Online Disease Diagnosis with Inductive Heterogeneous Graph Convolutional Networks

Authors:

Shao-Lun Huang,

Yefeng ZhengAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3349 - 3358

https://doi.org/10.1145/3442381.3449795

Published: 03 June 2021 Publication History

Abstract

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph when confronting users with scarce descriptions, hence yield better diagnosis accuracy. At last, we validate the superiority of our model on a large-scale EHR dataset.

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https://doi.org/10.1109/TCSS.2023.3270569
Zhang PZhou LWang LChen HXiao Q(2024)Multi-masks and Bi-spaces Reconstruction based Single-Layer Auto-encoder for Heterogeneous Graph Representation Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650586(1-9)Online publication date: 30-Jun-2024
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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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Cited By

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https://doi.org/10.1109/TCSS.2023.3270569
Zhang PZhou LWang LChen HXiao Q(2024)Multi-masks and Bi-spaces Reconstruction based Single-Layer Auto-encoder for Heterogeneous Graph Representation Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650586(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650586
Zhang HLv HTan YMa GWang FYang C(2024)ExpertODE: Continuous Diagnosis Prediction with Expert Enhanced Neural Ordinary Differential Equations2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687504(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687504
Li ZRuan T(2024)Knowledge-Routed Automatic Diagnosis With Heterogeneous Patient-Oriented GraphIEEE Access10.1109/ACCESS.2024.341241912(89573-89584)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3412419
Oss Boll HAmirahmadi AGhazani MMorais WFreitas ESoliman AEtminani FByttner SRecamonde-Mendoza M(2024)Graph neural networks for clinical risk prediction based on electronic health recordsJournal of Biomedical Informatics10.1016/j.jbi.2024.104616151:COnline publication date: 2-Jul-2024
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Li CLiu XWang CLiu YYu WShao JYuan Y(2024)GTP-4o: Modality-Prompted Heterogeneous Graph Learning for Omni-Modal Biomedical RepresentationComputer Vision – ECCV 202410.1007/978-3-031-73235-5_10(168-187)Online publication date: 30-Sep-2024
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