Mortality prediction in ICU Using a Stacked Ensemble Model
Open Access
- 28 November 2022
- journal article
- research article
- Published by Hindawi Limited in Computational and Mathematical Methods in Medicine
- Vol. 2022, 1-12
- https://doi.org/10.1155/2022/3938492
Abstract
Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Innovative algorithms play a crucial role in boosting the performance of models. This study uses a stacked ensemble model to predict mortality in ICU by incorporating the clinical severity scoring results, in which several machine learning algorithms are employed to compare the performance. The experimental results show that the stacked ensemble model achieves good performance compared with the model without integrating the severity scoring results, which has the area under curve (AUC) of 0.879 and 0.862, respectively. To improve the performance of prediction, two feature subsets are obtained based on different feature selection techniques, labeled as SetS and SetT. Evaluation performances show that the SEM based on the SetS achieves a higher AUC value (0.879 and 0.860). Finally, the SHapley Additive exPlanations (SHAP) analysis is employed to interpret the correlation between the risk features and the outcome.Keywords
Funding Information
- National Natural Science Foundation of China (62103105, 12201108, 12171085, 12161050, 1107010306, 1108000245, 1108000241, BK20200347, BK20210218, 2242020R40073, 2242022k30038, MCCSE2021B02, 2242020R10053, 2020A1515110129)
This publication has 30 references indexed in Scilit:
- MIMIC-III, a freely accessible critical care databaseScientific Data, 2016
- ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication for Patient OutcomesManagement Science, 2015
- Explaining prediction models and individual predictions with feature contributionsKnowledge and Information Systems, 2013
- Training and assessing classification rules with imbalanced dataData Mining and Knowledge Discovery, 2012
- pROC: an open-source package for R and S+ to analyze and compare ROC curvesBMC Bioinformatics, 2011
- MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneityBMC Bioinformatics, 2011
- mice: Multivariate Imputation by Chained Equations inRJournal of Statistical Software, 2011
- Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*Critical Care Medicine, 2006
- klaR Analyzing German Business CyclesPublished by Springer Science and Business Media LLC ,2005
- The APACHE III Prognostic SystemSocial psychiatry. Sozialpsychiatrie. Psychiatrie sociale, 1991