Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty – a development and validation study

Background Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 25; H. 1; S. 106 - 13
Hauptverfasser:	Langenberger, Benedikt, Schrednitzki, Daniel, Halder, Andreas, Busse, Reinhard, Pross, Christoph
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 03.03.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Aged Algorithms Arthroplasty (hip) Arthroplasty (knee) Arthroplasty, Replacement, Hip - statistics & numerical data Arthroplasty, Replacement, Knee - statistics & numerical data Artificial intelligence Body mass index Body size Clinical trials Datasets Duration of surgery Female Health aspects Health Informatics Hip Hospitals Humans Infections Information Systems and Communication Service Joint replacement surgery Knee Learning algorithms Machine Learning Male Management of Computing and Information Systems Medicine Medicine & Public Health Middle Aged Missing data Patient safety Patient-reported outcome measures Patients Regression Regression analysis Retrospective Studies Risk factors Surgeons Surgery Time Factors Validation studies Variables Germany Patient-reported outcome measures Machine learning Duration of surgery
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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