Graph Neural Network-Based Diagnosis Prediction
Abstract
Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms, but facing data insufficiency and noise problem. Recently, more accurate and robust medical knowledge-guided methods have been proposed and have achieved superior performance. These methods inject the knowledge from a graph structure medical ontology into deep models via attention mechanisms to provide supplementary information of the input data. However, these methods only partially leverage the knowledge graph and neglect the global structure information, which is an important feature. To address this problem, we propose an end-to-end robust solution, namely Graph Neural Network-Based Diagnosis Prediction (GNDP). First, we propose to utilize the medical knowledge graph as an internal information of a patient by constructing sequential patient graphs. These graphs not only carry the historical information from the EHR but also infuse with domain knowledge. Then we design a robust diagnosis prediction model based on a spatial-temporal graph convolutional network. The proposed model extracts meaningful features from sequential graph EHR data effectively through multiple spatial-temporal graph convolution units to generate robust patients' representations for accurate diagnosis predictions. We evaluate the performance of GNDP against a set of state-of-the-art methods on two real-world medical data sets, the results demonstrate that our methods can achieve a better utilization of knowledge graph and improve the accuracy on diagnosis prediction tasks.
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- Authors
- Li, Y., Qian, B., Zhang, X., & Liu, H.
- Affiliations
- Xi'an Jiaotong University (CN)
- Corresponding author
- Li, Yang
- Venue
- Big Data
- Publisher
- SAGE Publications
- Published
- 2020-10-01
- Volume
- 8
- Issue
- 5
- Pages
- 379-390
- ISSN
- 2167-6461 (ISSN-L)
- 2167-647X
- OpenAlex
- W3048964647
- Cited by
- 83
- References
- 4
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 22 · 2023: 15 · 2022: 11 · 2021: 7
- FWCI
- 3.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Big Data (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- diagnostic prediction, electronic health records, graph neural networks, medical knowledge graph, spatio-temporal graph convolutional network, medical ontologies, patient representation learning
- MeSH terms
- Deep Learning, Algorithms, Computer Graphics, Diagnosis, Computer-Assisted, Medical Informatics, Neural Networks, Computer, Electronic Health Records
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