Clinical Decision Support Software for Prediction of Postoperative Atrial Fibrillation Following Bypass Surgery

The problem of accurately predicting the onset of sustained postoperative atrial

fibrillation (AF) in patients undergoing coronary artery bypass grafting (CABG) remains open.

Investigators have reported many clinical indices currently associated with postoperative AF

following CABG. Contemporary machine learning techniques are well-suited to recognizing

underlying trends in ‘training’ data consisting of several labeled examples, and to using the

results to classify new unlabeled data with remarkable sensitivity and specificity. We propose

the development of advanced clinical decision support software capable of automatically

gathering and analyzing relevant clinical data from patients undergoing CABG in order to

provide physicians with objective and non-invasive insights into the likelihood of sustained

postoperative AF so that patient morbidity and mortality, as well as healthcare costs, can be

significantly reduced by targeting appropriate preventative therapies.

Faculty Supervisor:

Selim Akl

Student:

Partner:

Queen's University

Discipline:

Computer science

Sector:

Education

University:

Queen's University

Program:

Accelerate

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