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Lookup NU author(s): Melvin JoyORCiD
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Introduction: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensemble algorithm in predicting mortality. Methods: The study was a cohort of adults admitted to a medical unit of a tertiary care hospital with sepsis. The data were divided into a training data set (70%) and a test data set (30%). Boruta algorithm was used to identify important features. In the first phase of stacked ensemble model, weak learners such as random forest (RF), support vector machine (SVM), elastic net, and gradient boosting machine were trained. The SVM was used in phase 2 as meta learner to combine the results of all weak learners. All models were validated using test data. Results: In our cohort of 1,453 patients, the mortality rate was 27% (95% confidence interval [CI]: 25, 29). The Boruta algorithm identified inotrope use and assisted ventilation as the most important variables, which could predict mortality. The random forest outperforms (area under the curve [AUC]: 97.91%) the other algorithms. The AUCs for the other models are SVM (95.21%), GBM (93.67%), and GLM net (91.42%). However, the stacking of all the above models had an AUC of 92.14%. In the test data set, the accuracy of all methods including the RF method accuracy decreased (92.6 to 85.5%). Conclusions: The random forest showed high accuracy in train and moderate accuracy in the test data. We suggest more regional open‑access intensive care databases that can aid making machine learning a bigger support for healthcare personnel.
Author(s): Babu M, Sappani M, Joy M, Chandiraseharan VK, Jeyaseelan L, Sudarsanam TD
Publication type: Article
Publication status: Published
Journal: Journal of Postgraduate Medicine
Year: 2024
Volume: 70
Issue: 4
Pages: 209-216
Print publication date: 06/12/2024
Online publication date: 06/12/2024
Acceptance date: 03/10/2024
ISSN (print): 0022-3859
ISSN (electronic): 0972-2823
Publisher: Wolters Kluwer - Medknow
URL: https://doi.org/10.4103/jpgm.jpgm_357_24
DOI: 10.4103/jpgm.jpgm_357_24
PubMed id: 39641373
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