Accurate and reproducible prediction of ICU readmissions
Nguyen, D.-P.; Paris, N.; Parrot, A.
Show abstract
Readmission in the intensive care unit (ICU) is associated with poor clinical outcomes and high costs. Traditional scoring methods to help clinicians deciding whether a patient is ready for discharge have failed to meet expectations, paving the way for machine learning based approaches. Freely available datasets such as MIMIC-III have served as benchmarking media to compare such tools. We used the OMOP-CDM version of MIMIC-III (MIMIC-OMOP) to train and evaluate a lightweight tree boosting method to predict readmission in ICU at different time points after discharge (3, 7 and 30 days), outperforming existing solutions with an AUROC of 0.805 for 3-days readmission.
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