Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.

Journal: BMC musculoskeletal disorders

PMID: 40340665

Abstract

PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.

Authors

Juhan Oh

Eulji University School of Medicine, Daejeon, Republic of Korea.
Minah Park

Department of Ophthalmology, Soonchunhyang University Hospital Cheonan, Soonchunhyang University College of Medicine, 31 Soonchunhyang 6-Gil Dongnam-Gu, Cheonan, 31151, Republic of Korea.
Yonghan Cha

Department of Orthopaedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, South Korea.
Jae-Hyun Kim

Institute for Digital Life Convergence, Dankook University, Cheonan, Republic of Korea.
Seung Hoon Kim

Department of Ophthalmology, Soonchunhyang University Hospital Cheonan, Soonchunhyang University College of Medicine, 31 Soonchunhyang 6-Gil Dongnam-Gu, Cheonan, 31151, Republic of Korea. immergru@gmail.com.

Keywords

Aged Aged, 80 and over Female Hip Fractures Humans Kaplan-Meier Estimate Machine Learning Male Patient Readmission Retrospective Studies Risk Assessment Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40340665)

Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.

Abstract

Authors

Keywords

External Resources

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