将医疗法规转化为数字：美国、欧盟和中国十年来医疗器械监管变化的向量化
Transforming Medical Regulations into Numbers: Vectorizing a Decade of Medical Device Regulatory Shifts in the USA, EU, and China
AI总结：
本研究利用NLP和BERT对中美欧十年来664项医疗器械法规进行向量化与NER分析，结合LDA主题模型比较了各阶段监管要求，揭示了跨国监管的语义差异与一致性，为监管科学提供了计算分析新视角。
摘要：
确保医疗器械安全性和有效性的监管框架的导航可能具有挑战性，尤其是在不同地区之间。这些框架通常需要冗余测试，从而减缓了将创新成果提供给患者的进程。本研究利用自然语言处理（NLP）分析了过去十年中来自美国、欧盟和中国的664项法规和指南，涵盖超过2亿个tokens。我们将法规划分为关键阶段，例如动物研究、临床试验和其他测试阶段，并使用来自Transformer的双向编码器表示（BERT）执行命名实体识别（NER），以识别关键的监管术语和实体。通过将这些文本转换为数值表示，并按阶段、国家和年份进行分段，我们比较了各司法管辖区的要求并评估了它们的一致性。此外，我们应用潜在狄利克雷分配（LDA）进行主题分析，以观察监管重点随时间的变化，反映了不断演变的优先事项和挑战。我们的分析揭示了不同国家和阶段之间显著的语义相似性和差异。例如，动物研究法规中最接近的一致性存在于中国和美国之间，平均余弦距离为0.33。这些发现突显了监管科学中的计算潜力，为研究人员、政策制定者和行业专业人士提供了有价值的见解。
英文摘要：
Navigating the regulatory frameworks that ensure the safety and efficacy of medical devices can be challenging, especially across different regions. These frameworks often require redundant testing, slowing down the process of getting innovations to patients. This study leverages Natural Language Processing (NLP) to analyze 664 regulations and guidelines from the USA, EU, and China over the past decade, covering over 200 million tokens. We categorize regulations into key phases, such as animal studies, clinical trials, and other testing stages, and use Bidirectional Encoder Representations from Transformers (BERT) to perform Named Entity Recognition (NER), identifying key regulatory terms and entities. By converting these texts into numerical representations and segmenting them by phase, country, and year, we compare jurisdictional requirements and assess their alignment. Additionally, we apply Latent Dirichlet Allocation (LDA) for theme analysis to observe changes in regulatory focus over time, reflecting evolving priorities and challenges. Our analysis reveals notable semantic similarities and differences between countries and phases. For instance, the closest alignment in animal study regulations is between China and the USA, with a mean cosine distance of 0.33. These findings highlight the computational potential in regulatory science, offering valuable insights for researchers, policymakers, and industry professionals.
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。
