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Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs

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Abstract

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial–temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial–temporal synchronous graph convolution block to capture the spatial, temporal, and spatial–temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients’ future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

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Data availability

The MIMIC-III dataset are publicly available at https://mimic.mit.edu/. The MedClin dataset is not currently available as a private dataset.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful suggestions. Our work is supported by the National Key Research and Development Program of China (Grant No. 2020AAA0109400).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Weiguang Wang, Yingying Feng & Xia Zhang

  2. Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110167, Liaoning, China

    Weiguang Wang, Wei Cai & Xia Zhang

  3. School of Computer Science, Peking University, Beijing, 100871, China

    Haiyan Zhao

  4. Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, 100871, China

    Haiyan Zhao

  5. College of Intelligence and Computing, Tianjin University, Tianjin, 300354, China

    Xin Wang

  6. Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China

    Ruikai Cai

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  1. Weiguang Wang
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  4. Xin Wang
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  7. Xia Zhang
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Contributions

WW designed the study, performed measurements, designed the analysis, and wrote the manuscript. YF designed the schema for PKG and extracted the data. HZ designed the analysis and the schema for PKG. XW designed the analysis. RC cleaned the clinical data. WC designed the schema for PKG. XZ designed the study and the analysis. All authors contributed to the article and approved the submitted version.

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Correspondence to Xia Zhang.

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Wang, W., Feng, Y., Zhao, H. et al. Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs. Health Inf Sci Syst 12, 15 (2024). https://doi.org/10.1007/s13755-024-00278-7

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