This paper is in the following e-collection/theme issue:

Machine Learning (2018) Stroke (231) Clinical Information and Decision Making (1860) Decision Support for Health Professionals (1500) Secondary Use of Clinical Data for Research and Surveillance (463)

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

Authors of this article:

Sandrine Müller1 Author Orcid Image ;   Susanne Diekmann1 Author Orcid Image ;   Markus Wenzel1, 2 Author Orcid Image ;   Horst Karl Hahn1, 3 Author Orcid Image ;   Johannes Tuennerhoff4, 5 Author Orcid Image ;   Ulrike Ernemann6 Author Orcid Image ;   Florian Hennersdorf6 Author Orcid Image ;   German Stroke Registry Investigators ;   Max Westphal1 Author Orcid Image ;   Sven Poli4, 5 Author Orcid Image
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