A machine learning framework to adjust for learning effects in medical device safety evaluation.

ABSTRACT:

Objectives

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

Materials and methods

A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attri

SUBMITTER: Koola JD 

PROVIDER: S-EPMC11648715 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature