Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.

Gianluca Brugnara; Ulf Neuberger; Mustafa A Mahmutoglu; Martha Foltyn; Christian Herweh; Simon Nagel; Silvia Schönenberger; Sabine Heiland; Christian Ulfert; Peter Arthur Ringleb; Martin Bendszus; Markus A Möhlenbruch; Johannes Pfaff; Philipp Vollmuth

doi:10.1161/STROKEAHA.120.030287

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.

Gianluca Brugnara, Ulf Neuberger, Mustafa A Mahmutoglu, Martha Foltyn, Christian Herweh, Simon Nagel, Silvia Schönenberger, Sabine Heiland, Christian Ulfert, Peter Arthur Ringleb, Martin Bendszus, Markus A Möhlenbruch, Johannes Pfaff (Co-Autor/-in), Philipp Vollmuth

Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

97 Quellenangaben (Web of Science)

Originalsprache	Englisch
Seiten (von - bis)	3541-3551
Fachzeitschrift	STROKE
Jahrgang	51
Ausgabenummer	12
DOIs	https://doi.org/10.1161/STROKEAHA.120.030287
Publikationsstatus	Veröffentlicht - 2020

Zugriff auf Dokument

10.1161/STROKEAHA.120.030287

Dieses zitieren

Brugnara, G., Neuberger, U., Mahmutoglu, M. A., Foltyn, M., Herweh, C., Nagel, S., Schönenberger, S., Heiland, S., Ulfert, C., Ringleb, P. A., Bendszus, M., Möhlenbruch, M. A., Pfaff, J., & Vollmuth, P. (2020). Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. STROKE , 51(12), 3541-3551. https://doi.org/10.1161/STROKEAHA.120.030287

Brugnara, Gianluca ; Neuberger, Ulf ; Mahmutoglu, Mustafa A et al. / Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. in: STROKE 2020 ; Jahrgang 51, Nr. 12. S. 3541-3551.

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title = "Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.",

keywords = "CT PERFUSION, THROMBECTOMY, THERAPY",

author = "Gianluca Brugnara and Ulf Neuberger and Mahmutoglu, {Mustafa A} and Martha Foltyn and Christian Herweh and Simon Nagel and Silvia Sch{\"o}nenberger and Sabine Heiland and Christian Ulfert and Ringleb, {Peter Arthur} and Martin Bendszus and M{\"o}hlenbruch, {Markus A} and Johannes Pfaff and Philipp Vollmuth",

year = "2020",

doi = "10.1161/STROKEAHA.120.030287",

language = "English",

volume = "51",

pages = "3541--3551",

journal = "STROKE ",

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publisher = "Lippincott Williams & Wilkins ",

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Brugnara, G, Neuberger, U, Mahmutoglu, MA, Foltyn, M, Herweh, C, Nagel, S, Schönenberger, S, Heiland, S, Ulfert, C, Ringleb, PA, Bendszus, M, Möhlenbruch, MA, Pfaff, J & Vollmuth, P 2020, 'Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.', STROKE , Jg. 51, Nr. 12, S. 3541-3551. https://doi.org/10.1161/STROKEAHA.120.030287

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. / Brugnara, Gianluca; Neuberger, Ulf; Mahmutoglu, Mustafa A et al.
in: STROKE , Jahrgang 51, Nr. 12, 2020, S. 3541-3551.

Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

TY - JOUR

T1 - Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.

AU - Brugnara, Gianluca

AU - Neuberger, Ulf

AU - Mahmutoglu, Mustafa A

AU - Foltyn, Martha

AU - Herweh, Christian

AU - Nagel, Simon

AU - Schönenberger, Silvia

AU - Heiland, Sabine

AU - Ulfert, Christian

AU - Ringleb, Peter Arthur

AU - Bendszus, Martin

AU - Möhlenbruch, Markus A

AU - Pfaff, Johannes

AU - Vollmuth, Philipp

PY - 2020

Y1 - 2020

KW - CT PERFUSION

KW - THROMBECTOMY

KW - THERAPY

U2 - 10.1161/STROKEAHA.120.030287

DO - 10.1161/STROKEAHA.120.030287

M3 - Original Article

C2 - 33040701

SN - 0039-2499

VL - 51

SP - 3541

EP - 3551

JO - STROKE

JF - STROKE

IS - 12

ER -

Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S et al. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. STROKE . 2020;51(12):3541-3551. doi: 10.1161/STROKEAHA.120.030287