Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset

Abstract:
        Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient’s LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.

DOI: 10.3390/app13126930

Projects: Prediction model of Length of Stay and probability of rehospitalization

Publication type: Journal article

Journal: Applied Sciences

Human Diseases: No Human Disease specified

Citation: Applied Sciences 13(12):6930

Date Published: 1st Jun 2023

Registered Mode: by DOI

Authors: Lars Hempel, Sina Sadeghi, Toralf Kirsten

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Citation
Hempel, L., Sadeghi, S., & Kirsten, T. (2023). Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset. In Applied Sciences (Vol. 13, Issue 12, p. 6930). MDPI AG. https://doi.org/10.3390/app13126930
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Created: 25th Sep 2023 at 14:38

Last updated: 25th Sep 2023 at 14:39

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