A5078986788- Heart Failure Treatment and Management
- Sepsis Diagnosis and Treatment
- Cardiac Arrest and Resuscitation
- Machine Learning in Healthcare
- Cardiac pacing and defibrillation studies
- Cardiac Structural Anomalies and Repair
- Mechanical Circulatory Support Devices
- Artificial Intelligence in Healthcare
Assistance Publique – Hôpitaux de Paris
2024
Hôpital Lariboisière
2023-2024
Inserm
2024
While few traditional scores are available for risk stratification of patients hospitalized acute heart failure (AHF), the potential benefit machine learning (ML) is not well established. We aimed to assess feasibility and accuracy a supervised ML model including environmental factors predict in-hospital major adverse events (MAEs) in AHF. In April 2021, French national prospective multicentre study included all consecutive intensive cardiac care unit. Patients admitted AHF were analyses. A...
Abstract Background Acute heart failure (AHF) is a leading cause of mortality worldwide and major public health issue with still high rate in-hospital outcomes. Physicians need more investigations new tools to prevent those high-risk patients from adverse events (MAE). While few scores are available for risk stratification hospitalized AHF using traditional statistical methods, the potential benefit machine-learning (ML) not established. Purpose To investigate feasibility accuracy model...