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

I-Corps：用于医疗器械历史和召回的机器学习工具

• 批准号: 2334058
• 负责人: Soumya Sen
• 金额: $ 5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334058&HistoricalAwards=false
• 关键词: Corps; Machine; Learning; Tool; Medical

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.

该I-Corps项目的更广泛的影响/商业潜力是开发软件系统，以预测医疗设备召回可能性。在2003年至2020年之间，由于可能导致严重的健康问题甚至死亡的问题，在美国召回了8.9％（4,889）的医疗设备。这些召回会影响患者的结果，设备制造商和健康均能确保造成巨大的财务损失。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。但是，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并预测召回。拟议的技术使用先进的数据分析来研究医疗设备不良事件的历史，并为召回进行预测性建模。基于数据分析的系统可以通过避免出现故障和设备召回来改善对患者，医疗设备制造商，确保，监管机构和其他利益相关者的好处，对医疗设备召回可能性的评估。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设备召回可能性。提出的技术是一种在线决策支持系统，它使用数据分析来从FDA的510（k）设备批准文件中提取见解，以研究和预测召回。拟议的系统使用自然语言处理自动从设备文件中提取相关信息，并使用监督的机器学习对医疗设备召回的预测建模。该系统利用了相关的前身设备的特性以及设备引用网络的功能，以帮助制造商和分析师探索不同医疗设备的召回可能性。最初的结果表明，该系统可能会及时预测设备召回，这可以提高患者的安全性并减少医疗保健生态系统中几个利益相关者的财务损失。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来获得的支持。