Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Journal: Artificial intelligence in medicine
Published Date: Aug 1, 2023

Abstract

OBJECTIVE: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data.

Authors

  • Mingxuan Liu
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Siqi Li
    Software College, Northeastern University, Shenyang 110819, China.
  • Han Yuan
    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Marcus Eng Hock Ong
    Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore. marcus.ong.e.h@sgh.com.sg.
  • Yilin Ning
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
  • Feng Xie
    School of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: 1111705006@mail2.gdut.edu.cn.
  • Seyed Ehsan Saffari
    Duke-NUS Medical School, National University of Singapore, Singapore.
  • Yuqing Shang
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Victor Volovici
    Department of Neurosurgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands. v.volovici@erasmusmc.nl.
  • Bibhas Chakraborty
    Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Nan Liu
    Duke-NUS Medical School Centre for Quantitative Medicine Singapore Singapore.

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