Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (9): 88-95.doi: 10.11896/jsjkx.250300012

• Intelligent Medical Engineering • Previous Articles     Next Articles

DHMP:Dynamic Hypergraph-enhanced Medication-aware Model for Temporal Health EventPrediction

WU Hanyu1,2, LIU Tianci1,2, JIAO Tuocheng3, CHE Chao1,2   

  1. 1 Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education,Dalian University,Dalian,Liaoning 116622,China
    2 School of Software Engineering,Dalian University,Dalian,Liaoning 116622,China
    3 Zhongxin International College of Engineering,Shenyang Jianzhu University,Shenyang 110168,China
  • Received:2025-03-03 Revised:2025-07-01 Online:2025-09-15 Published:2025-09-11
  • About author:WU Hanyu,born in 1999,postgraduate.His main research interests include medical informatics and data mining.
    CHE Chao,born in 1981,Ph.D,professor.His main research interests include medical informatics,natural language processing and mining.
  • Supported by:
    National Natural Science Foundation of China(62076045),111 Project(D23006),Dalian Major Projects of Basic Research(2023JJ11CG002) and National Foreign Expert Project of China(D20240244).

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Abstract: Temporal health event prediction remains a fundamental challenge in medical Al.To address the critical problem of modeling complex medication-diagnosis relationships in EHR data this paper propose the DHMP model.Firstly,a dynamic subgraph learning mechanism captures local di-sease progression patterns.Secondly,a novel multi-hypergraph fusion architecture jointly models drug interactions and diagnosis associations.Finally,a temporal attention algorithm deciphering long-term depen-dencies in clinical records.Extensive experiments on MIMIC-III and MIMIC-IV datasets demonstrate that DHMP model has state-of-the-art performance,achieving 26.68% w-F1 in diagnosis prediction and 90.65% AUC in risk prediction.Clinical evaluation shows 89% consistency between model predictions and medical expertise,proving its reliability for decision support.

Key words: Dynamic subgraph learning, Graph neural network, Drug interaction, Temporal health event prediction, Clinical decision support

CLC Number: 

  • TP391
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