SIEPH: Safe Information Extraction from Patient Histories

医疗记录是发现人类健康的重要资源。症状、处方和其他临床事件的模式可以帮助研究人员理解为什么不同的患者对药物的反应不同，并导致对疾病的新理解。关于患者病史的大量信息记录在书面临床记录中。不幸的是，这些临床笔记通常不能被医学研究人员使用，因为它们可能包含敏感的个人信息。这直接限制了自然语言处理（NLP）方法的适用性，无法使用计算机自动阅读笔记并提取它们。该项目提出建立新的方法，安全地从医学研究人员所需的临床记录中提取重要信息，以回答可能导致新发现的复杂医学问题。为了实现这一目标，该项目将开发一种新的方法，使用合成记录的概念，合成记录是人工生成的医疗记录，在结构和内容上类似于真实的记录，但不包含任何敏感信息。然后，这些合成记录可以提供给医学研究人员，他们可以注释他们想要从真实的医疗记录中提取的确切信息类型。这些注释可用于构建机器学习系统，以从真实的医疗记录中提取特定类型的信息。由此产生的数据将被进一步扫描，以确保没有敏感信息泄露给研究人员，从而为他们提供进行医学发现所需的医学数据，但不会危及患者隐私。由此产生的技术将使医学研究人员能够对医疗记录提出新的复杂问题，他们所需要的信息被锁定在书面临床笔记中。我们将与NHS安全港团队合作，评估这种方法，以便将来可以帮助医学研究人员和NHS。

Medical records are an important resource for making discoveries about human health. Patterns of symptoms, prescriptions and other clinical events can help researchers understand why different patients respond differently to drugs and lead to new understandings about disease. Substantial information about a patient's medical history is recorded in written clinical notes. Unfortunately, these clinical notes frequently cannot be used by medical researchers because they may contain sensitive personal information. This directly limits the applicability of natural language processing (NLP) methods to use computers to automatically read the notes and extract them. This project proposes to build new methods that will safely extract important information from clinical notes that are needed by medical researchers to answer complex medical questions that could lead to new discoveries. To achieve this, the project will develop a novel method using the concept of synthetic records that are artificially generated medical records that resemble real records in structure and content but do not contain any sensitive information. These synthetic records can then be provided to medical researchers who can annotate the exact type of information that they want to pull from real medical records. These annotations can be used to build a machine learning system to extract the specific type of information from real medical records. The resulting data will be further scanned to ensure that no sensitive information is leaked to researchers thereby providing them with the medical data they need to make medical discoveries but not endangering patient privacy. The resulting technologies will enable medical researchers to ask new complex questions of medical records where the information they need is locked in written clinical notes. We will work with the NHS Safe Havens team to evaluate this approach so that it may aid medical researchers and the NHS in the future.