Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

Journal: Journal of cachexia, sarcopenia and muscle
PMID: 39994910

Abstract

BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, providing a unique opportunity to monitor changes in physical activity, a key factor related to the risk of falls in hospitals. This study is aimed at utilizing RTLS data to capture dynamic patient movements, integrating it with clinical information through a machine learning approach to enhance in-hospital fall predictions.

Authors

  • Dong Won Kim
    Department of Artificial Intelligence, Sungkyunkwan University College of Computing and Informatics, Suwon-si, Gyeonggi, South Korea.
  • Jihoon Seo
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sujin Kwon
    Department of Endocrinology, Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Chan Min Park
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Changho Han
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Yujeong Kim
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Korea.
  • Jaewoong Kim
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Chul Sik Kim
    Department of Endocrinology, Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Seok Won Park
    Department of Endocrinology, Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Dukyong Yoon
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kyoung Min Kim
    Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea.

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