Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials

Aims: To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and...

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Main Authors: Hidde Dijkstra, Jacobien H. F. Oosterhoff, Anouk van de Kuit, Frank F. A. IJpma, Joseph H. Schwab, Rudolf W. Poolman, Sheila Sprague, Sofia Bzovsky, Mohit Bhandari, Marc Swiontkowski, Emil H. Schemitsch, Job N. Doornberg, Laurent A. M. Hendrickx, On behalf of the Machine Learning Consortium, the HEALTH Investigators, and the FAITH Investigators
Format: Article
Language:English
Published: The British Editorial Society of Bone & Joint Surgery 2023-03-01
Series:Bone & Joint Open
Subjects:
machine learning
hip fracture
artificial intelligence
prediction models
shared decision-making
femoral neck fractures
hip fractures
brier scores
total hip arthroplasty (tha)
hemiarthroplasty
logistic regression
composite
anesthesiologists
clinicians
cancer
Online Access:https://online.boneandjoint.org.uk/doi/epdf/10.1302/2633-1462.43.BJO-2022-0162.R1

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https://online.boneandjoint.org.uk/doi/epdf/10.1302/2633-1462.43.BJO-2022-0162.R1

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