In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow

TL;DR

This paper introduces a big data analytics and machine learning-NLP workflow to efficiently identify, analyze, and interpret medical device recall initiators from large, multi-format datasets, enhancing recall management.

Contribution

The study develops a novel ML-NLP tool utilizing unsupervised clustering and text similarity classification for comprehensive recall initiator analysis in medical devices.

Findings

DBSCAN clustering effectively identifies recall initiators.

Text similarity classification aids in group size control.

The tool captures detailed and interconnected recall information.

Abstract

Recall initiator identification and assessment are the preliminary steps to prevent medical device recall. Conventional analysis tools are inappropriate for processing massive and multi-formatted data comprehensively and completely to meet the higher expectations of delicacy management with the increasing overall data volume and textual data format. This study presents a bigdata-analytics-based machine learning-natural language processing work tool to address the shortcomings in dealing efficiency and data process versatility of conventional tools in the practical context of big data volume and muti data format. This study identified, assessed and analysed the medical device recall initiators according to the public medical device recall database from 2018 to 2024 with the ML-NLP tool. The results suggest that the unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm can present each single recall initiator in a specific manner, therefore helping practitioners to identify the recall reasons comprehensively and completely within a short time frame. This is then followed by text similarity-based textual classification to assist practitioners in controlling the group size of recall initiators and provide managerial insights from the operational to the tactical and strategical levels. This ML-NLP work tool can not only capture specific details of each recall initiator but also interpret the inner connection of each existing initiator and can be implemented for risk identification and assessment in the forward SC. Finally, this paper suggests some concluding remarks and presents future works. More proactive practices and control solutions for medical device recalls are expected in the future.