Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

Journal: Journal of medical Internet research
PMID: 38157232

Abstract

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.

Authors

  • Hung-Hsun Chen
    Center of Teaching and Learning Development, National Chiao Tung University, Taiwan.
  • Henry Horng-Shing Lu
    Shing-Tung Yau Center, National Chiao Tung University, 1001 University Road, Hsinchu City, Taiwan. hslu@stat.nctu.edu.tw.
  • Wei-Hung Weng
    Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, 4th Floor, Boston, MA, 02115, USA. ckbjimmy@mit.edu.
  • Yu-Hsuan Lin
    Children's Research Institute, Departments of Pediatrics and Internal Medicine, Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

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