Machine learning for pattern detection in cochlear implant FDA adverse event reports. (1st November 2020)

• Record Type: Journal Article
• Authors: Crowson, Matthew G.; Hamour, Amr; Lin, Vincent; Chen, Joseph M.; Chan, Timothy C. Y.
• Is Part Of: Cochlear implants international. Volume 21:Number 6(2020)
• Page Start: 313
• Page End: 322
• Publication Date: 2020-11-01
• Subjects: Cochlear implants -- Machine learning -- Adverse events; Cochlear implants -- Periodicals; 617.882
• DOI: 10.1080/14670100.2020.1784569
• Languages: English
• ISSNs: 1467-0100

Abstract

Abstract : Importance: Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: Adverse event reports for the top three CI manufacturers were acquired for the analysis. Four supervised machine learning algorithms were used to predict which adverse event description pattern corresponded with a specific cochlear implant manufacturer and adverse event type. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Classification prediction accuracy (% correct predictions). Results: Most adverse events involved patient injury ( n = 16, 736), followed by device malfunction ( n = 10, 760), and death ( n = 16). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.