Integrating structured and unstructured clinical data significantly improved atrial fibrillation prediction in ischemic stroke patients using machine learning models.
Does the integration of structured and unstructured clinical text using ensemble machine learning improve the prediction of atrial fibrillation in patients with ischemic stroke?
Integrating unstructured clinical text with structured EMR data using ensemble machine learning improves the prediction of atrial fibrillation in patients with ischemic stroke.
Absolute Event Rate: 0% vs 0%
This study demonstrated the development of predictive models for AF in patients with ischemic stroke. Notably, the integration of structured variables with variables derived from unstructured clinical text improved predictive performance in selected model configurations. Rigorous internal and external validation processes confirmed the superior performance of ensemble learning-based machine learning models compared with alternative algorithms, underscoring the potential of this approach for AF risk prediction.
Chen et al. (Tue,) reported a other. Integrating structured and unstructured clinical data significantly improved atrial fibrillation prediction in ischemic stroke patients using machine learning models.
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