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Validation of syncope short-term outcomes prediction by machine learning models in an Italian emergency department cohort.

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Bibliographic Details
Title: Validation of syncope short-term outcomes prediction by machine learning models in an Italian emergency department cohort.
Authors: Levra AG; Department of Cardiovascular Medicine, Humanitas Research Hospital, IRCCS, Rozzano, Milan, Italy.; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy., Gatti M; IBM, Milan, Italy., Mene R; Roberto Mene Electrophysiology Unit, Niguarda Hospital, Milan, Italy., Shiffer D; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.; Emergency Department, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy., Costantino G; Emergency Department, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy., Solbiati M; Emergency Department, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy., Furlan R; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. raffaello.furlan@hunimed.eu.; Internal Medicine, Syncope Unit, IRCCS. Humanitas Research Hospital, Via A. Manzoni, 56, Rozzano, 20089, Milan, Italy. raffaello.furlan@hunimed.eu., Dipaola F; Internal Medicine, Syncope Unit, IRCCS. Humanitas Research Hospital, Via A. Manzoni, 56, Rozzano, 20089, Milan, Italy.
Source: Internal and emergency medicine [Intern Emerg Med] 2025 Aug; Vol. 20 (5), pp. 1597-1605. Date of Electronic Publication: 2025 Jul 16.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Springer Country of Publication: Italy NLM ID: 101263418 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1970-9366 (Electronic) Linking ISSN: 18280447 NLM ISO Abbreviation: Intern Emerg Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1970-9366
DOI:10.1007/s11739-025-04034-x