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Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data

Year 2021, Volume: 1 Issue: 2, 48 - 53, 30.09.2021

Abstract

Patients’ length of stay (LOS) in intensive care units (ICU) is an important factor for managing limited ICU resources such as beds, staffing, medicines, and medical devices. The goal of this study predicts that the ICU length of stay of patients is more than 3 days or not with Support Vector Machine (SVM), Logistic Regression (LR), XGBoost classifiers. We retrieved the 37,600 ICU
patients’ demographics data and last measured vital signs in their first 12 hours of stay from the MIMIC-III database. We filled the missing patients' data with three missing data imputation methods, namely k-nearest neighbor imputation (KNN), multivariate imputation by chained equations (MICE), and SoftImpute. Our results indicated that filling missing data with the Soft-Impute yielded the highest AUC score for all classifiers. We obtained the highest area under the curve score as 66.1% with the XGBoost classifier and Soft-Impute missing data imputation.

References

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There are 19 citations in total.

Details

Primary Language English
Subjects Clinical Sciences, Engineering
Journal Section Research Articles
Authors

Zeliha Ergul Aydın This is me

Zehra Kamışlı Öztürk This is me

Publication Date September 30, 2021
Published in Issue Year 2021 Volume: 1 Issue: 2

Cite

APA Ergul Aydın, Z., & Kamışlı Öztürk, Z. (2021). Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data. Artificial Intelligence Theory and Applications, 1(2), 48-53.