Please use this identifier to cite or link to this item: https://doi.org/10.1097/MD.0000000000014197
Title: Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department
Authors: Chiew, C.J.
Liu, N. 
Tagami, T. 
Wong, T.H.
Koh, Z.X.
Ong, M.E.H. 
Chung, F.-T..
Keywords: electrocardiography
emergency service
hospital
machine learning
sepsis
triage
Issue Date: 2019
Publisher: Lippincott Williams and Wilkins
Citation: Chiew, C.J., Liu, N., Tagami, T., Wong, T.H., Koh, Z.X., Ong, M.E.H., Chung, F.-T.. (2019). Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department. Medicine (United States) 98 (6) : e14197. ScholarBank@NUS Repository. https://doi.org/10.1097/MD.0000000000014197
Rights: Attribution-NonCommercial 4.0 International
Abstract: Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ?2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools. © 2019 the Author(s). Published by Wolters Kluwer Health, Inc..
Source Title: Medicine (United States)
URI: https://scholarbank.nus.edu.sg/handle/10635/210003
ISSN: 0025-7974
DOI: 10.1097/MD.0000000000014197
Rights: Attribution-NonCommercial 4.0 International
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