Currently submitted to: JMIR Medical Informatics
Date Submitted: Aug 20, 2019
Open Peer Review Period: Aug 20, 2019 - Aug 28, 2019
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Machine learning prediction of cardiac arrest in emergency department: sequential characteristics for clinical validity
The development and application of clinical prediction models using machine learning in clinical decision support systems has attracted increasing attention.
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.
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.
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.
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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