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Combining Machine Learning With Real-World Data to Identify Gaps in Clinical Practice Guidelines: Feasibility Study Using the Prospective German Stroke Registry and the National Acute Ischemic Stroke Guidelines

Combining Machine Learning With Real-World Data to Identify Gaps in Clinical Practice Guidelines: Feasibility Study Using the Prospective German Stroke Registry and the National Acute Ischemic Stroke Guidelines

The hyperparameter search space included the number of estimators (n_estimators: 50‐500) and the maximum depth of trees (max-depth: 1‐20). A total of 5 hyperparameter configurations were randomly sampled, balancing exploration with computational efficiency. The model was evaluated across different combinations of features, and the best-performing configuration from the process was selected for final evaluation on the validation set.

Sandrine Müller, Susanne Diekmann, Markus Wenzel, Horst Karl Hahn, Johannes Tuennerhoff, Ulrike Ernemann, Florian Hennersdorf, German Stroke Registry Investigators, Max Westphal, Sven Poli

JMIR Med Inform 2025;13:e69282

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