A transformer-based framework for temporal health event prediction with graph-enhanced representations.

Journal: Journal of biomedical informatics
Published Date: May 3, 2025

Abstract

OBJECTIVE: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imbalances and temporal irregularities in admission records.

Authors

  • Tianci Liu
    School of Mechanical Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
  • Lizhong Liang
  • Chao Che
    Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, China.
  • Yunjiong Liu
    Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education, Dalian University, Dalian, 116622, Liaoning, China; School of Software Engineering, Dalian University, Dalian, 116622, Liaoning, China.
  • Bo Jin
    HBISolutions Inc., Palo Alto, CA 94301, USA.

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