CUbiC@ASU is engaged in an active collaboration with a high-volume cardiology practice catering to patients across Arizona - Advanced Cardiac Specialists - to design and develop computational tools and frameworks for cardiac decision support. While there are publicly available heart risk score calculators, we plan to develop a framework that allows for risk propagation, as a patient goes through different diagnostic tests in the clinical pathway. We are currently working on risk stratification for patients who have undergone a Percutaneous Coronary Intervention (PCI) procedure.

This project surfaces several research challenges in multimedia, pattern recognition, clinical machine learning and natural language processing. The fields of pattern recognition and machine learning are founded on the definition of distance metrics between data points that are analyzed. A fundamental problem that we are tackling at CUbiC is the design of inter-patient distance metrics that combine patient data with related ontologies to provide consistency and semantics in the metrics. Also, a very important requirement in applying pattern recognition algorithms to the field of medical diagnosis, more than any other field, is a measure of confidence or belief of an algorithm in its result. We are working on theoretical frameworks to compute confidence measure values that can be calibrated for these scenarios. We believe that this design provides a co-aptive system that collaborates with physicians, rather than replacing their skill and expertise.