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Cited by 86Sepsis Diagnosis and TreatmentMachine Learning in HealthcareArtificial Intelligence in Healthcare

An intelligent warning model for early prediction of cardiac arrest in sepsis patients

Layeghian Javan, Samaneh, Sepehri, Mohammad Mehdi, Layeghian Javan, Malihe, Khatibi, Toktam·2019·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2019.06.010
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Abstract

Background Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult sepsis patients. Moreover, the potential of some techniques, including ensemble algorithms, has not yet been addressed in improving the prediction outcomes. It is required to find methods for generating high-performance predictions with sufficient time lapse before the arrest. In this regard, various variables and parameters should also been examined.Objective The aim was to use machine learning in order to propose a cardiac arrest prediction model for adult patients with sepsis. It is required to predict the arrest several hours before the incidence with high efficiency. The other goal was to investigate the effect of the time series dynamics of vital signs on the prediction of cardiac arrest.Method 30 h clinical data of every sepsis patients were extracted from Mimic III database (79 cases, 4532 controls). Three datasets (multivariate, time series and combined) were created. Various machine learning models for six time groups were trained on these datasets. The models included classical techniques (SVM, decision tree, logistic regression, KNN, GaussianNB) and ensemble methods (gradient Boosting, XGBoost, random forest, balanced bagging classifier and stacking). Proper solutions were proposed to address the challenges of missing values, imbalanced classes of data and irregularity of time series.Results The best results were obtained using a stacking algorithm and multivariate dataset (accuracy = 0.76, precision = 0.19, sensitivity = 0.77, f1-score = 0.31, AUC= 0.82). The proposed model predicts the arrest incidence of up to six hours earlier with the accuracy and sensitivity over 70%.Conclusion We illustrated that machine learning techniques, especially ensemble algorithms have high potentials to be used in prognostic systems for sepsis patients. The proposed model, in comparison with the exiting warning systems including APACHE II and MEWS, significantly improved the evaluation criteria. According to the results, the time series dynamics of vital signs are of great importance in the prediction of cardiac arrest incidence in sepsis patients.

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Authors
Layeghian Javan, S., Sepehri, M. M., Layeghian Javan, M., & Khatibi, T.
Affiliations
Tarbiat Modares University (IR)
Corresponding author
Sepehri, Mohammad Mehdi
ORCID
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2019-09
Volume
178
Pages
47-58
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2019.06.010
OpenAlex
W2949974618
Cited by
86
References
29
Citations by year
2026: 7 · 2025: 15 · 2024: 11 · 2023: 17 · 2022: 13 · 2021: 14 · 2020: 7 · 2019: 2
FWCI
6.85
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Computer Methods and Programs in Biomedicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
sepsis, cardiac arrest prediction, early warning systems, ensemble learning, stacking classifier, class imbalance, missing data, MIMIC-III
MeSH terms
Machine Learning, Adolescent, Adult, Algorithms, Decision Trees, Female, Heart Arrest, Humans, Male, Middle Aged, Sensitivity and Specificity, Severity of Illness Index, Reproducibility of Results, Incidence, Multivariate Analysis, Normal Distribution, Case-Control Studies, Monitoring, Ambulatory, Sepsis, APACHE, Young Adult, Vital Signs, Electronic Health Records

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