  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.
