Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69282, first published .
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

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

Sandrine Müller   1 , MSc ;   Susanne Diekmann   1 , Dr med ;   Markus Wenzel   1, 2 , Prof Dr-Ing ;   Horst Karl Hahn   1, 3 , Prof Dr-Ing ;   Johannes Tuennerhoff   4, 5 , Dr med ;   Ulrike Ernemann   6 , Prof Dr Med ;   Florian Hennersdorf   6 , Dr med ;   German Stroke Registry Investigators   ;   Max Westphal   1 , Dr rer nat ;   Sven Poli   4, 5 , Prof Dr Med

1 Fraunhofer Institute for Digital Medicine, Bremen, Germany

2 Constructor University, Bremen, Germany

3 Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany

4 Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany

5 Hertie Institute for Clinical Brain Research, Tübingen, Germany

6 Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany

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