Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study

Abstract

BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.ObjectiveOne way to address these issues is through the use of artificial intelligence for the detection of medical device failure. This goal of this study was to develop automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections.MethodsThe system developed in this study utilised machine learning techniques as its core. Throughout the study four algorithms were utilised. These algorithms include Decision Tree, Random Forest, Linear Regression and Support Vector Machines.ResultsFinal results showed that Random Forest algorithms had the best performance on various metrics among the four developed models. It achieved accuracy of 94% and precision and recall of 70% and 93% respectively.ConclusionThis study shows that use of systems like the one developed in this study have the potential to improve management and maintenance of medical devices.

Keywords: artificial intelligence; classification; healthcare; machine learning; medical device.