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

Zeliha Ergul Aydın; Zehra Kamışlı Öztürk

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

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.

Keywords

References

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Details

Primary Language

English

Subjects

Clinical Sciences, Engineering

Journal Section

Research Article

Authors

Zeliha Ergul Aydın ^* This is me
Türkiye

Zehra Kamışlı Öztürk This is me
Türkiye

Publication Date

September 30, 2021

Submission Date

June 9, 2021

Acceptance Date

June 25, 2021

Published in Issue

Year 2021 Volume: 1 Number: 2

IZ

https://izlik.org/JA95WM84JY

Cite

RIS / Bibtex

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. https://izlik.org/JA95WM84JY

AMA

1.Ergul Aydın Z, Kamışlı Öztürk Z. Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data. AITA. 2021;1(2):48-53. https://izlik.org/JA95WM84JY

Chicago

Ergul Aydın, Zeliha, and Zehra Kamışlı Öztürk. 2021. “Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data”. Artificial Intelligence Theory and Applications 1 (2): 48-53. https://izlik.org/JA95WM84JY.

EndNote

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

IEEE

[1]Z. Ergul Aydın and Z. Kamışlı Öztürk, “Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data”, AITA, vol. 1, no. 2, pp. 48–53, Sept. 2021, [Online]. Available: https://izlik.org/JA95WM84JY

ISNAD

Ergul Aydın, Zeliha - Kamışlı Öztürk, Zehra. “Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data”. Artificial Intelligence Theory and Applications 1/2 (September 1, 2021): 48-53. https://izlik.org/JA95WM84JY.

JAMA

1.Ergul Aydın Z, Kamışlı Öztürk Z. Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data. AITA. 2021;1:48–53.

MLA

Ergul Aydın, Zeliha, and Zehra Kamışlı Öztürk. “Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data”. Artificial Intelligence Theory and Applications, vol. 1, no. 2, Sept. 2021, pp. 48-53, https://izlik.org/JA95WM84JY.

Vancouver

1.Zeliha Ergul Aydın, Zehra Kamışlı Öztürk. Prediction Length of Stay in Intensive Care Unit in the Presence of Missing Data. AITA [Internet]. 2021 Sep. 1;1(2):48-53. Available from: https://izlik.org/JA95WM84JY