Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.

Journal: BMC geriatrics
Published Date: May 24, 2022

Abstract

BACKGROUND: Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture.

Authors

  • Nitchanant Kitcharanant
    Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
  • Pojchong Chotiyarnwong
    . Department of Orthopedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Thiraphat Tanphiriyakun
    Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
  • Ekasame Vanitcharoenkul
    Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, 10700, Bangkok, Thailand.
  • Chantas Mahaisavariya
    Golden Jubilee Medical Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Wichian Boonyaprapa
    Siriraj Information Technology Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Aasis Unnanuntana
    . Department of Orthopedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.

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