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

该I-Corps项目更广泛的影响/商业潜力是开发一种软件系统，以预测医疗器械召回的可能性。 2003年至2020年，美国有8.9%（4，889件）的医疗器械因可能导致严重健康问题甚至死亡而被召回。这些召回会影响患者的治疗结果，设备制造商和医疗保险公司都会遭受巨大的经济损失。及时预测召回可能会使医疗保健系统中的多个利益相关者受益。然而，制造商目前依靠自己的实验室研究，专有数据和客户反馈来评估设备安全性和预测召回。 该技术使用先进的数据分析来研究医疗设备的不良事件历史，并对召回进行预测建模。 这个基于数据分析的系统可以改善医疗器械召回可能性的评估，通过避免故障和器械召回，使患者、医疗器械制造商、保险公司、监管机构和其他利益相关者受益。这个I-Corps项目基于数据分析平台的开发，该平台使用来自多个数据源的历史数据来可视化和分析医疗器械召回可能性。 该技术是一种在线决策支持系统，使用数据分析从FDA的510（k）设备批准文件中提取见解，以研究和预测召回。所提出的系统使用自然语言处理从设备文件中自动提取相关信息，并使用监督机器学习对医疗设备召回进行预测建模。 该系统利用相关前代器械的特征和器械引用网络的功能，帮助制造商和分析师探索不同医疗器械的召回概率。初步结果表明，该系统可以在及时预测设备召回方面提供显着的好处，这可能会提高患者的安全性，并减少医疗生态系统中几个利益相关者的经济损失。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.