In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow
This addresses the need for more efficient and versatile data processing tools in medical device recall management, though it is incremental as it applies existing ML-NLP methods to a specific domain.
The study tackled the problem of identifying and assessing medical device recall initiators by developing a machine learning-natural language processing workflow that processes large, multi-format data from 2018 to 2024, using DBSCAN clustering and text similarity classification to help practitioners quickly and comprehensively identify recall reasons and provide managerial insights.
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.