SIEPH: Safe Information Extraction from Patient Histories

SIEPH：从患者病史中安全提取信息

• 批准号: EP/X018237/1
• 负责人: Graham McDonald
• 金额: $ 25.76万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX018237%2F1
• 关键词: SIEPH; Safe; Information; Extraction; Patient

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.

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了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。有关患者病史的大量信息记录在书面临床笔记中。不幸的是，医学研究人员可能经常使用这些临床笔记，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）方法使用计算机自动读取注释并提取它们的适用性。该项目建议建立新的方法，这些方法将安全从临床注释中安全地提取重要信息，这些信息是医学研究人员所需的，以回答可能导致新发现的复杂医疗问题。为了实现这一目标，该项目将使用合成记录的概念来开发一种新颖的方法，该方法是人为生成的医疗记录，类似于结构和内容的真实记录，但不包含任何敏感信息。然后可以将这些综合记录提供给可以注释他们想从实际病历中获取的确切信息类型的医学研究人员。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。将进一步扫描所得数据，以确保不会向研究人员泄漏敏感信息，从而为他们提供他们进行医疗发现所需的医疗数据，但不会危及患者隐私。最终的技术将使医学研究人员能够在书面临床注释中锁定所需的信息的新复杂问题。我们将与NHS Safe Havens团队合作评估这种方法，以便将来帮助医学研究人员和NHS。