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

Müller, Sandrine; Diekmann, Susanne; Wenzel, Markus; Hahn, Horst Karl; Tünnerhoff, Johannes; Ernemann, Ulrike; Hennersdorf, Florian; Westphal, Max; Poli, Sven

dc.contributor.author	Tünnerhoff, Johannes
dc.contributor.author	Ernemann, Ulrike
dc.contributor.author	Hennersdorf, Florian
dc.contributor.author	Poli, Sven
dc.date.accessioned	2026-04-21T11:48:15Z
dc.date.available	2026-04-21T11:48:15Z
dc.date.issued	2025
dc.identifier.issn	2291-9694
dc.identifier.uri	http://hdl.handle.net/10900/178498
dc.language.iso	en	de_DE
dc.publisher	Toronto : Jmir Publications, Inc	de_DE
dc.relation.uri	http://dx.doi.org/10.2196/69282	de_DE
dc.subject.ddc	004	de_DE
dc.subject.ddc	610	de_DE
dc.title	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	de_DE
dc.type	Article	de_DE
utue.quellen.id	20251118000000_04227
utue.publikation.seiten	Article e69282	de_DE
utue.personen.roh	Müller, Sandrine
utue.personen.roh	Diekmann, Susanne
utue.personen.roh	Wenzel, Markus
utue.personen.roh	Hahn, Horst Karl
utue.personen.roh	Tünnerhoff, Johannes
utue.personen.roh	Ernemann, Ulrike
utue.personen.roh	Hennersdorf, Florian
utue.personen.roh	Westphal, Max
utue.personen.roh	Poli, Sven
dcterms.isPartOf.ZSTitelID	Jmir Medical Informatics	de_DE
dcterms.isPartOf.ZS-Volume	13	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE