MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Planned participants: 200000
• From 1 Apr 2024 to the primary completion date, Mar 2027
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to - · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

Sites in registry order. Pick your city on the disease page to sort them by distance.