Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
复制标题
DOI：
10.1038/s41746-020-0258-y
复制
发表时间：
2020-04-14
影响因子：
15.2
通讯作者：
Butte, Atul J.
中科院分区：
文献类型：
--
作者：
Norgeot, Beau;Muenzen, Kathleen;Butte, Atul J.
除了结构化医疗记录数据中可用数据的缺乏之外，人们越来越需要确定患者在疾病进展、实际护理实践、病理、不良事件等方面的确切状态。现在，确定这些难以获取的数据元素对于复杂性状的准确表型、不良结果的检测、超说明书用药的疗效以及患者的纵向监测至关重要。临床记录通常包含有关个体患者、疾病的细微差别、医生选择的治疗策略以及由此产生的结果的最详细和相关的数字信息。然而，注释在很大程度上仍未用于研究，因为它们包含受保护的健康信息（PHI），它与个人识别数据同义。以前的临床记录去识别方法非常僵化，而且仍然太不准确，无法在现实世界中得到任何实质性的使用，这主要是因为它们接受的医学文本语料库太小。为了构建新的去识别化工具，我们创建了最大的 PHI 手动注释临床记录语料库，并开发了名为 Philter（“受保护的健康信息过滤器”）的可定制开源去识别化软件。在这里，我们描述了 Philter 的设计和评估，并展示了它如何比以前的方法在现实世界中提供实质性的改进。
There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.