# Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2009.02625 · HTML: https://mlsanity.com/paper/2009.02625/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Heterogeneous Graph Masked Autoencoders](https://mlsanity.com/paper/2208.09957/) (2022)
- [Federated Graph Semantic and Structural Learning](https://mlsanity.com/paper/2406.18937/) (2024)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision](https://mlsanity.com/paper/2206.14719/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)
- [Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2206.03659/) (2022)
- [APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification](https://mlsanity.com/paper/2512.18473/) (2025)

## Abstract (arXiv)

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.