An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.

Journal: BMC medical informatics and decision making
PMID: 39962480

Abstract

BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges remain in choosing the most appropriate method, deciding how many top-ranked variables to include, and ensuring these selections are meaningful from a medical perspective.

Authors

  • Hongnian Wang
    School of Management, Jinan University, Guangzhou, China.
  • Mingyang Zhang
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Liyi Mai
    Institute of Sciences in Emergency Medicine, Department of Emergency Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Abdelouahab Bellou
    Institute of Sciences in Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China. abellou402@gmail.com.
  • Lijuan Wu
    Big Data Decision Institute, Jinan University, Guangzhou, China.

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