In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based sensitive variable identification systems that work on one database fail on another. To address this issue, our paper explores the use of machine learning algorithms to identify sensitive variables in structured data, thus facilitating the de-identification process. We made a key observation that the distributions of metadata of PHI fields and non-PHI fields are very different. Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data. We trained the model on a variety of large EHR databases from different data sources and found that our algorithm achieves 99% accuracy when detecting PHI-related fields for unseen datasets. The implications of our study are significant and can benefit industries that handle sensitive data.

翻译：在大数据时代，医疗提供者、社区及研究人员对数据共享与协作的需求日益增长，旨在改善健康结果、产生有价值见解并推动研究进展。1996年《健康保险可携性与责任法案》（HIPAA）作为联邦法律，通过定义受保护健康信息（PHI）的监管规则来保护敏感健康信息，但该法案并未为数据共享前检测或移除PHI提供高效工具。该研究领域面临的挑战之一是不同机构间数据中PHI字段的异质性——这种变异性导致基于规则的敏感变量识别系统在某个数据库有效时，在另一数据库可能失效。为应对此问题，本文探索利用机器学习算法识别结构化数据中的敏感变量，从而助力去标识化流程。我们观察到PHI字段与非PHI字段的元数据分布存在显著差异。基于这一发现，我们从原始特征的元数据中提取了超过30个特征，并运用机器学习构建分类模型，自动识别结构化电子健康记录（EHR）数据中的PHI字段。通过使用来自不同数据源的多类型大型EHR数据库训练模型，我们的算法在检测未见过数据集中的PHI相关字段时实现了99%的准确率。本研究的意义重大，可惠及处理敏感数据的相关行业。