Associazione Nazionale Medici Cardiologi Ospedalieri

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CONGRESS ABSTRACT

CONGRESS ABSTRACT

Categorie
ROLE OF THE MACHINE LEARNING-DERIVED HYPOTENSION INDEX (HPI) TO CONTAIN INTRAOPERATIVE HYPOTENSION DURING TRANSCATHETER EDGE TO EDGE REPAIR PROCEDURES
Anno:
2024
Introduction Percutaneous endovascular valvular interventions can result in profound hemodynamic instability, elevated burden of intraoperative hypotension (IOH) and related postoperative complications: ischemic stroke, acute kidney injury and increased mortality. Machine learning(ML),a branch of Artificial intelligence (AI), can analyze large volumes of data, find associations and allowing predictive rather than…
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PREDICTIVE MACHINE LEARNING MODEL FOR MECHANICAL DILATATION IN TRANSVENOUS LEAD EXTRACTION PROCEDURES
Anno:
2024
BACKGROUND Transvenous lead extraction (TLE) remains a procedure that requires a high level of expertise, with a doubled risk of death and clinical failure when performed in low-volume centers compared to high-volume ones. PURPOSE The aim of this study was to create a machine learning (ML)-based risk stratification system…
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Machine learning approach for prediction of clinical outcomes in anticoagulated patients with atrial fibrillation
Anno:
2024
Background: Despite the availability of different risk scores, the accuracy of actual prediction tools for outcomes in patients with atrial fibrillation (AF) remains modest. Although Machine Learning (ML) has been used to predict outcomes in the AF population, evidences about outcome prediction in a population entirely on oral anticoagulation…
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