MAchine Learning to Boost the Early diagnosis of acute Cardiovascular conditions (MALBEC)

The overall objectives of this project are to improve the early diagnosis and management of ACVD. Therefore, this project has two main aims: first, to develop and implement a clinical decision support tool that integrates and visualizes results of established diagnostic variables (e.g., ST-segment elevation in the 12-lead ECG) in a dashboard and, second, to derive and validate human-simulatability machine learning (ML) models that integrate information from the three main diagnostic pillars to rapidly inform the diagnostic probability for six ACVD in patients with acute chest pain and/or acute dyspnoea.