Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection

1 code implementation • 24 Apr 2024 • Elena Albu, Shan Gao, Pieter Stijnen, Frank Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben van Calster

We included data from 27478 admissions to the University Hospitals Leuven, covering 30862 catheter episodes (970 CLABSI, 1466 deaths and 28426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) outcomes to predict the 7-day CLABSI risk.

Length of Stay prediction for Hospital Management using Domain Adaptation

no code implementations • 29 Jun 2023 • Lyse Naomi Wamba Momo, Nyalleng Moorosi, Elaine O. Nsoesie, Frank Rademakers, Bart De Moor

In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain.

Domain Adaptation Length-of-Stay prediction +1