Machine Learning to Augment Cardio-Oncology Medical Practice Operations and Advance Medical Science

Machine Learning to Augment Cardio-Oncology Medical Practice Operations and Advance Medical Science

Overview: 

Advances in oncology help more patients survive and lead longer/better lives, but many survivors struggle with cardiovascular diseases during/post cancer therapies, caused by cardiotoxicity. A new medical subspecialty called cardio-oncology helps alleviate cardiotoxicity concerns, but it suffers from lack of consistency in referrals from oncologists to cardiologists, limited availability of standard guidelines, and absence of a dedicated analytical support. Machine learning can help identify and refer at-risk patients in reliable and equitable ways but may also pave new ways for scientific inquiry through insights derived from data. We will review these distinct pathways in applying ML to support cardio-oncology research and medical practice.

Speaker:

Jacob Krive, PhD has extensive professional HIT experience and combines his leadership responsibilities with teaching and scholarly work in academia. He operationally leads Clinical Analytics team at NorthShore University Health System and has academic appointments as Clinical Associate Professor at University of Illinois at Chicago in Biomedical and Health Information Sciences, College of Medicine, UI Cancer Center, and Discovery Partners Institute. He is also affiliated with Nova Southeastern University as informatics faculty and University of Chicago Pritzker School of Medicine as a researcher. Dr. Krive’s research interests include medical artificial intelligence, clinical analytics, healthcare data science curriculum, and economics of medical informatics.