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Related Experiment Video

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Predicting Ischemic Stroke in Patients with Atrial Fibrillation Using Machine Learning.

Seonwoo Jung1, Min-Keun Song2, Eunjoo Lee3

  • 1Department of ICT Convergence System Engineering, Chonnam National University, 61186 Gwangju, Republic of Korea.

Frontiers in Bioscience (Landmark Edition)
|March 29, 2022
PubMed
Summary

This study developed a deep learning model to predict ischemic stroke in atrial fibrillation (AF) patients using Korean health data. The model achieved higher accuracy than existing methods, aiding in stroke prevention strategies.

Keywords:
atrial fibrillationattentiondeep neural networkmachine learningnational health insurance servicestroke

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Area of Science:

  • Cardiology
  • Neurology
  • Medical Informatics

Background:

  • Atrial fibrillation (AF) is a significant risk factor for stroke.
  • Accurate stroke risk prediction is crucial for preventing initial and recurrent cerebrovascular events.
  • Early intervention strategies rely on precise risk stratification in AF patients.

Purpose of the Study:

  • To develop a machine learning model for predicting ischemic stroke in patients diagnosed with atrial fibrillation.
  • To leverage the extensive Korean National Health Insurance (KNHIS) database for robust risk prediction.
  • To identify key features associated with ischemic stroke occurrence in the AF population.

Main Methods:

  • Extracted 65-dimensional features from 754,949 AF patients in the KNHIS database.
  • Employed logistic regression to identify statistically significant predictors of ischemic stroke.
  • Constructed an attention-based deep neural network for ischemic stroke prediction.

Main Results:

  • Identified 48 features significantly associated with ischemic stroke (p < 0.001).
  • The deep learning model achieved a higher AUROC of 0.727 ± 0.003 compared to CHA2DS2-VASc score (0.651 ± 0.007).
  • The model demonstrated superior predictive performance over other machine learning methods in a validation cohort of 150,989 AF patients.

Conclusions:

  • The developed deep learning model shows promise for predicting ischemic stroke risk in AF patients.
  • This approach can enhance preventive medicine by providing personalized stroke risk scores.
  • Identifying associated features aids AF patients in proactive stroke prevention planning.