Bioinformatics Commons^™

A Machine Learning Approach To Post-Market Surveillance Of Medical Devices, Jonathan Bates, Shu-Xia Li, Craig Parzynski, Ronald Coifman, Harlan Krumholz, Joseph Ross

Yale Day of Data

Post-market surveillance is a collection of processes and activities used by product manufacturers and regulators, such as the U.S. Food and Drug Administration (FDA) to monitor the safety and effectiveness of medical devices once they are available for use “on the market”. These activities are designed to generate information to identify poorly performing devices and other safety problems, accurately characterize real-world device performance and clinical outcomes, and facilitate the development of new devices, or new uses for existing devices. Typically, a device is monitored by comparing adverse events in the exposed population to a matched unexposed population. This research considers …

Applying Novel Tree-Based Frameworks To Big Data For Classification Of Heart Failure Patients And Prediction Of Clinical Responses, Yan Zhang, Nicholas Downing, Emily Bucholz, Suganthi Balasubramanian, Shu-Xia Li, Tara Liptak, Harlan Krumholz, Mark Gerstein

Yale Day of Data

Over 5 million Americans suffer from heart failure, a condition with a 5-year survival that eclipses all cancers apart from that of lung cancer. Conventional understanding of heart failure is simplistic: it is viewed as a single syndrome, despite real heterogeneity. In addition, models predicting outcomes focus on dichotomous results, like 30-day readmission. A novel approach to classification of heart failure may improve our ability to target interventions, improve patient experiences, and predict outcomes.

The Healthcare Cost and Utilization Project is a family of administrative claims databases that describes patient demographics, comorbidities, procedures, acute care utilization and outcomes, such as …