A comparison of modeling approaches for static and dynamic prediction of central line-associated bloodstream infections using electronic health records (part 2): random forest models

Abstract Objective Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of static and dynamic random forest (RF) models to pre...

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Principais autores:	Elena Albu, Shan Gao, Pieter Stijnen, Frank E Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben Van Calster
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2025-07-01
coleção:	Diagnostic and Prognostic Research
Assuntos:	Random forests Competing risks Survival CLABSI EHR Dynamic prediction
Acesso em linha:	https://doi.org/10.1186/s41512-025-00194-8
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