MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
University Hospital, Basel, Switzerland
200,000 participants
Apr 1, 2024
OBSERVATIONAL
Conditions
Summary
The research project aims to develop clinical decision support tools integrating established diagnostic variables and machine learning (ML) models for rapid diagnosis of acute life-threatening cardiovascular conditions in emergency department (ED) patients with chest pain or dyspnea with the ultimate goal of Improved diagnostic accuracy, faster patient management, and reduced medical errors.
Eligibility
Inclusion Criteria1
- Acute cardiovascular disease (ACVD)
Exclusion Criteria3
- age < 18 years old
- patients presenting in cardiogenic shock
- chronic terminal kidney failure requiring dialysis
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Interventions
MALBEC will be delivered through five integrated work packages (WP) encompassing: (0) platform development and implementation, (1) data pooling, (2) model development, (3) performance comparison, (4) performance validation, and (5) platform plugin
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06927791