Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Aug 20, 2019
Open Peer Review Period: Aug 20, 2019 - Aug 28, 2019
Date Accepted: Jul 14, 2020
(closed for review but you can still tweet)
Machine learning prediction of cardiac arrest in emergency department: sequential characteristics for clinical validity
ABSTRACT
Background:
The development and application of clinical prediction models using machine learning in clinical decision support systems has attracted increasing attention.
Objective:
The aim of this study is to develop a prediction model for cardiac arrest using machine learning and sequential characteristics in emergency department (ED) and to perform validations for clinical usefulness.
Methods:
This retrospective study was conducted for ED patients from a tertiary academic hospital that suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The dataset was randomly allocated to the development cohort (80%) and the validation cohort (20%). We trained three machine learning algorithms with repeated 10-fold cross-validation.
Results:
The main performance parameters were the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC = 0.97; AUPRC = 0.85) outperformed the recurrent neural network (AUROC = 0.96; AUPRC = 0.80) and the logistic regression algorithm (AUROC=0.92; AUPRC=0.72). The model performance over time was maintained with AUROC of at least 80% across monitoring time points during 24 hours before event occurrence.
Conclusions:
We developed a cardiac arrest prediction model using machine learning and sequential characteristics in ED. The model was validated for clinical usefulness using chronological visualisation focused on clinical usability.
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Copyright
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