Predicting In‐Hospital Fall Risk Using Machine Learning With Real‐Time Location System and Electronic Medical Records
This study underscores the capability of utilizing RTLS to predict in-hospital falls by tracking the changes of patients' physical activity through a machine learning approach. This may improve early fall risk detection during hospitalization, thereby preventing falls and enhancing patient safety.
Authors
- Chan Min Park
- Dukyong Yoon (ORCID: https://orcid.org/0000-0003-1635-8376)
- Seok Won Park (ORCID: https://orcid.org/0000-0002-6664-7128)
- Jaewoong Kim (ORCID: https://orcid.org/0000-0002-5706-0181)
- Ji-Hoon Seo (ORCID: https://orcid.org/0000-0003-3650-3104)
- Kyoung Min Kim (ORCID: https://orcid.org/0000-0001-8150-0266)
- Chul Sik Kim (ORCID: https://orcid.org/0000-0003-0855-6348)
- Yujeong Kim (ORCID: https://orcid.org/0000-0003-3674-993X)
- Dong Won Kim (ORCID: https://orcid.org/0000-0001-9716-2767)
- Changho Han (ORCID: https://orcid.org/0000-0003-4121-5465)
- Sujin Kwon (ORCID: https://orcid.org/0000-0002-8505-6462)
Institutions
- Yonsei University (KR)
- Severance Hospital (KR)
- Yonsei University Health System (KR)
Publication Details
- Journal
- Journal of Cachexia Sarcopenia and Muscle
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1002/jcsm.13713
- Citations
- 6
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 28.56
Funders
- Ministry of Health and Welfare
- National Research Foundation
- Ministry of Education
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea