MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Observational
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
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:
• • Acute cardiovascular disease (ACVD)
Locations
Other Locations
Switzerland
University Hospital Basel
RECRUITING
Basel
Contact Information
Primary
Jasper Boeddinghaus, PD Dr. med.
jasper.boeddinghaus@usb.ch
+41 61 32 87897
Backup
Ivo Strebel, PhD
ivo.strebel@usb.ch
Time Frame
Start Date: 2024-04-01
Estimated Completion Date: 2027-03
Participants
Target number of participants: 200000
Treatments
Patients with acute chest pain and/or acute dyspnoea
Patients with acute chest pain and/or acute dyspnoea
Related Therapeutic Areas
Sponsors
Leads: University Hospital, Basel, Switzerland
Collaborators: University of Basel