Rafael Cobas Paz

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

Fabrizio D’Ascenzo, Ovidio De Filippo, Guglielmo Gallone et al. · The Lancet · 2021 · 324 citations

Adverse Events, Health Informatics, Machine Learning +14

Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences

Job A.J. Verdonschot, Marco Merlo, Fernándo Domínguez et al. · European Heart Journal · 2020 · 116 citations · Full text

Development and external validation of a post-discharge bleeding risk score in patients with acute coronary syndrome: The BleeMACS score

Sergio Raposeiras‐Roubín, Jonas Faxén, Andrés Íñiguez et al. · International Journal of Cardiology · 2018 · 89 citations

Thrombosis, External Validation, Cardiovascular Disease +13

P2Y12 inhibitors in acute coronary syndrome patients with renal dysfunction: an analysis from the RENAMI and BleeMACS projects

Ovidio De Filippo, Fabrizio D’Ascenzo, Sergio Raposeiras‐Roubín et al. · European Heart Journal - Cardiovascular Pharmacotherapy · 2019 · 51 citations · Full text

Assessing the performance of the PRECISE-DAPT and PARIS risk scores for predicting one-year out-of-hospital bleeding in acute coronary syndrome patients

Emad Abu‐Assi, Sergio Raposeiras‐Roubín, Rafael Cobas Paz et al. · EuroIntervention · 2018 · 48 citations