Automating the identification of safety events involving machine learning-enabled medical devices

With growing use of machine learning (ML)-enabled medical devices by clinicians and consumers safety events involving these systems are emerging. Current analysis of safety events heavily relies on retrospective review by experts, which is time consuming and cost ineffective. This study develops automated text classifiers and evaluates their potential to identify rare ML safety events from the US FDA's MAUDE. Four stratified classifiers were evaluated using a real-world data distribution with different feature sets: report text; text and device brand name; text and generic device type; and all information combined. We found that stratified classifiers using the generic type of devices were the most effective technique when tested on both stratified (F1-score=85%) and external datasets (precision=100%). All true positives on the external dataset were consistently identified by the three stratified classifiers, indicating the ensemble results from them can be used directly to monitor ML events reported to MAUDE.