An attention-based deep neural network predicted the occurrence of ischemic stroke in patients with atrial fibrillation with a higher AUROC (0.727) compared to the CHA2DS2-VASc score (0.651).
Observational (n=754,949)
Does an attention-based deep neural network improve the prediction of ischemic stroke in patients with atrial fibrillation compared to the CHA2DS2-VASc score?
An attention-based deep neural network using national health insurance data predicted ischemic stroke in patients with atrial fibrillation with higher accuracy than the traditional CHA2DS2-VASc score.
Absolute Event Rate: 0.727% vs 0.651%
BACKGROUND: Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach. METHODS: We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model. RESULTS: -value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods. CONCLUSIONS: As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.
Jung et al. (2022) conducted an observational in Atrial Fibrillation (n=754,949). Attention-based deep neural network vs. CHA2DS2-VASc score was evaluated on Prediction of ischemic stroke occurrence (AUROC). An attention-based deep neural network predicted the occurrence of ischemic stroke in patients with atrial fibrillation with a higher AUROC (0.727) compared to the CHA2DS2-VASc score (0.651).