Previously submitted to: JMIR Medical Informatics (no longer under consideration since Mar 17, 2023)
Date Submitted: Feb 20, 2023
Open Peer Review Period: Feb 20, 2023 - Mar 17, 2023
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Use of machine learning techniques for phenotyping ischemic stroke instead of the rule-based methods: A nationwide population-based study
ABSTRACT
Background:
In South Korea, 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, assigning the I63 diagnosis code for admission, and incorporating the imaging or drug claims data. 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 National Health Insurance Ilsan Hospital database (n=30,897). The ML techniques were constructed based on the results of a previous chart review (gold standard), and the demographic variables such as qualifications, examination/screening, medical utilization, and total medical costs were used as features. 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, such as neural network based deep learning methods, 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 by utilizing the National Health Information Database.
Citation
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