I-Corps: A Machine Learning Tool for Medical Device History and Recalls

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a software system to predict medical device recall likelihood. Between 2003 and 2020, 8.9% (4,889) of medical devices in the US were recalled due to issues that could cause serious health problems or even death. These recalls impact patient outcomes, and both device manufacturers and health insurers incur large financial losses. Timely prediction of recalls may benefit multiple stakeholders in the healthcare system. However, manufacturers presently rely on their own lab studies, proprietary data, and customer feedback to evaluate device safety and predict recalls. The proposed technology uses advanced data analytics to study the history of adverse events of medical devices and performs predictive modeling for recalls. This data analytics-based system may improve the evaluation of medical device recall likelihood with benefits to patients, medical device manufacturers, insurers, regulators, and other stakeholders by avoiding malfunctions and device recalls. This I-Corps project is based on the development of a data analytics platform that uses historical data from multiple data sources to visualize and analyze medical device recall likelihood. The proposed technology is an online decision support system that uses data analytics to extract insights from FDA’s 510(k) device approval files to study and predict recalls. The proposed system uses natural language processing to automatically extract relevant information from device files and performs predictive modeling for medical device recalls using supervised machine learning. The system leverages the characteristics of related predecessor devices and features from the device citation network to help manufacturers and analysts explore recall probabilities of different medical devices. Initial results suggest that this system may provide significant benefits in predicting device recalls in a timely manner, which may improve patient safety and reduce financial losses for several stakeholders in the healthcare ecosystem. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.