Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

Journal: Sleep & breathing = Schlaf & Atmung
Published Date: Apr 29, 2024

Abstract

BACKGROUND: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder.

Authors

  • Hyeontae Jo
    Basic Science Research Institute, Pohang University of Science and Technology, Pohang, Republic of Korea.
  • Myna Lim
    Department of Information Science, Cornell University, Ithaca, NY, 14850, USA.
  • Hong Jun Jeon
    Department of Psychiatry, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea.
  • Junseok Ahn
    Department of Psychiatry, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea.
  • Saebom Jeon
    Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Marketing Bigdata, Mokwon University, Republic of Korea. Electronic address: alwaysns@mokwon.ac.kr.
  • Jae Kyoung Kim
    Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea. Electronic address: jaekkim@kaist.ac.kr.
  • Seockhoon Chung
    Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea. Electronic address: schung@amc.seoul.kr.

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