Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (9): 25-36.doi: 10.11896/jsjkx.250600104

• Intelligent Medical Engineering • Previous Articles     Next Articles

Research Progress on Multi-domain Adaptation Problems in Clinical Data Modeling

CHEN Xiu1, ZHANG Xinyun1, CHENG Yuting1, CHEN Wei1, HUANG Zhengxing3, LIU Zhenyu4, ZHANG Yuanpeng1,2   

  1. 1 Department of Medical Informatics,School of Medicine,Nantong University,Nantong,Jiangsu 226001,China
    2 Department of Health Technology and Informatics,The Hong Kong Polytechnic University,Hong Kong 999077,China
    3 College of Computer Science and Technology,Zhejiang University,Hangzhou 310058,China
    4 Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2025-06-14 Revised:2025-08-01 Online:2025-09-15 Published:2025-09-11
  • About author:CHEN Xiu,born in 2001,master.Her main research interest is intelligent medical engineering.
    ZHANG Yuanpeng,born in 1984,professor,Ph.D supervisor.His main research interest is medical artificial intelligence.
  • Supported by:
    “Qinglan Project” of Jiangsu Higher Education Institutions.

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Abstract: With the deep integration of artificial intelligence and healthcare,clinical data is undergoing a paradigm shift from “aiding decision-making” to “driving decision-making”.Clinical data encompasses both structured and unstructured information such as patient symptoms,diagnostic images,and treatment records,providing crucial support for medical decision-making.However,due to the prevalent “domain shift” phenomenon,the independent and identically distributed(i.i.d.) assumption,which clinical AI models rely on for training and evaluation,is invalidated,severely restricting the models’ cross-domain generalization ability.Domain adaptation and domain generalization techniques can effectively enhance the cross-domain performance of models.The former adjusts models use unlabeled target domain data to adapt them to new environments,the latter learns domain-invariant features based on source domain data to achieve generalization without target domain data.Regarding the application progress of these two types of techniques in clinical data modeling,this paper classifies them into shallow and deep methods,demonstrates their application scenarios across different data types,and summarizes the current performance differences of various methods in terms of generalization performance,data dependency,and interpretability.

Key words: Clinical data, Domain shift, Domain adaptation, Domain generalization, Data dependence

CLC Number: 

  • R319
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