Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.

Journal: BMC medical informatics and decision making
PMID: 39350186

Abstract

BACKGROUND: Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up schedule. Based on the 1-year postoperative outcomes, we aimed to use a machine-learning (ML) algorithm to predict 2-year postoperative outcomes.

Authors

  • Yechan Seo
    Department of Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Seoi Jeong
    Department of Transdisciplinary Medicine, Seoul National University Hospital, 101 Daehak-Ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Siyoung Lee
    Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Korea.
  • Tae-Shin Kim
    Department of Neurosurgery, Champodonamu Hospital, 32 Baumoe-ro 35-gil, Seocho-gu, Seoul, 03080, Republic of Korea.
  • Jun-Hoe Kim
    Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea.
  • Chun Kee Chung
    Interdisciplinary Program in Neuroscience, Graduate School, Seoul National University, Seoul, 151-742, Korea. chungc@snu.ac.kr.
  • Chang-Hyun Lee
    Department of Radiology, University of Iowa, Iowa City, IA 52242, USA.
  • John M Rhee
    Department of Orthopaedic Surgery, Emory University School of Medicine, Atlanta, GA, 30322, USA.
  • Hyoun-Joong Kong
    Department of Biomedical Engineering, Chungnam National University College of Medicine, Daejeon, Korea.
  • Chi Heon Kim
    Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea.

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