使用机器学习-自然语言处理工作流对医疗器械召回发起者的深入分析
In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow
AI总结：
本文提出基于大数据分析的 ML-NLP 工作流，利用2018–2024年公开召回数据、DBSCAN聚类与文本相似度分类识别并分析医疗器械召回发起者，为多层级风险管理提供支持。
摘要：
召回发起者的识别与评估是预防医疗器械召回的初步步骤。传统分析工具不适合全面、完整地处理海量且多格式的数据，难以在总体数据量和文本数据格式不断增加的情况下满足精细化管理的更高期望。本研究提出了一种基于大数据分析的机器学习-自然语言处理工作工具，以解决传统工具在大数据量和多数据格式的实际情境中处理效率与数据处理通用性方面的不足。本研究使用 ML-NLP 工具，依据2018年至2024年的公开医疗器械召回数据库，对医疗器械召回发起者进行了识别、评估和分析。结果表明，无监督的基于密度的带噪声应用空间聚类（Density-Based Spatial Clustering of Applications with Noise，DBSCAN）算法能够以具体方式呈现每一个单独的召回发起者，从而帮助从业人员在较短时间内全面、完整地识别召回原因。随后进行基于文本相似度的文本分类，以协助从业人员控制召回发起者的群组规模，并从运营层到战术层和战略层提供管理洞见。该 ML-NLP 工作工具不仅能够捕捉每个召回发起者的具体细节，还能解释各现有发起者之间的内在联系，并可用于前向供应链（SC）中的风险识别与评估。最后，本文给出了若干结论性意见并提出未来工作。预计未来将出现更主动的医疗器械召回实践和控制解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
