Use of machine learning techniques for phenotyping ischemic stroke instead of the rule-based methods: A nationwide population-based study

Abstract

Abstract Background Many studies have evaluated stroke using claims data; most of these studies have defined ischemic stroke by using an operational definition following the rule-based method. Rule-based methods tend to overestimate the number of patients with ischemic stroke. Objective We aimed to identify an appropriate algorithm for phenotyping stroke by applying machine learning (ML) techniques to analyze the claims data. Methods We obtained the data from the Korean National Health Insurance Service database, which is linked to the Ilsan Hospital database (n = 30,897). The performance of prediction models (extreme gradient boosting [XGBoost] or long short-term memory [LSTM]) was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under precision-recall curve (AUPRC), and calibration curve. Results In total, 30,897 patients were enrolled in this study, 3,145 of whom (10.18%) had ischemic stroke. XGBoost, a tree-based ML technique, had the AUROC was 93.63% and AUPRC was 64.05%. LSTM showed results similar to those of the rule-based method. The F1 score was 70.01%, while the AUROC was 97.10% and AUPRC was 85.70%, which was the highest. Conclusions We proposed recurrent neural network based deep learning techniques to improve stroke phenotyping. We anticipate the ability to produce rapid and accurate results.