Methods for the privacy preserving analysis of sensitive health data: text analysis and data visualisation

“数据革命”可以加强保健/社会护理，加速研究，并帮助我们评估改善健康和保健的新方法。但是，分析卫生数据的新方法必须以公众理解、满意并适当处理数据隐私和安全的方式使用。该奖学金将开发工具，帮助科学家和医生充分利用敏感的健康数据，同时尽量减少个人或其健康状况被了解的风险。我将重点讨论卫生数据使用的两个日益重要的领域：1)来自医学文本的信息；2)数据的可视化显示，特别是在增强现实（AR）或虚拟现实（VR）中。1)敏感文本分析医疗文本（如健康记录，医疗信件）包含患者随时间的数据，包括识别信息（如地址，近亲，完整的出生日期）。虽然对护理和研究有帮助，但出于隐私原因，敏感医学文本的使用受到严格控制。现有的方法从文本中提取信息，但可能通过删除可识别的数据或将患者分组来控制泄露风险。但这些程序并不是万无一失的：一些患者可能仍然是可识别的，在丢弃关键信息后，结果可能是错误的。我的奖学金采用了我们为免费软件包DataSHIELD开发的一种新方法。这允许在不被看到/复制的情况下分析敏感数据，并自动检测和阻止许多可能识别的分析。我早期的工作表明，DataSHIELD可以用于文本数据，我将扩展它，以保护通过基于计算机的文本挖掘工具从医学文本中提取的数据的隐私。这将显著增加分析的范围，可适用于医学文本，同时保持保密性。我将首先在合成（合成但真实）文本上工作，以安全地开发和测试新方法。一旦我对软件的工作感到满意，我将把它应用到萨拉·斯利尼特博士（纽卡斯尔大学药学院）的一个研究项目中，询问使用多种药物治疗的患者（“多种药物治疗”）的结果是否更差（例如更多的跌倒和住院）。如果他们这样做，就可以制定新的政策来控制多种药物并改善健康结果。2)敏感的数据可视化ar /VR技术提供了一种快速解释和理解健康数据的方法，无需特殊的技术/科学专业知识。这些沉浸式环境之所以有效，是因为它们可以同时呈现出比在纸上或屏幕上看到的更多关于某人的信息。但这也使个人更容易被识别。如果AR/VR得到广泛应用，我们必须正确理解披露风险，并制定防范措施。2015年，我们与行业合作伙伴Masters of Pie和Lumacode合作，赢得了在VR中展示惠康信托数据的比赛。我领导的正在进行的工作扩展了我们的工作，利用基于ALSPAC队列的合成数据探索VR视觉方法。我们一起构建了BigDataVR试点分析工具。该奖学金将探讨在使用BigDataVR等沉浸式环境时确定识别某人风险的因素。这些发现将用于开发通过DataSHIELD创建VR兼容图形的新方法，这些图形可以传达数据集的“本质”，而无需显示可能识别某人的完整数据。我将创建一个初步的概念证明，使用DataSHIELD将数据基础可视化发送到免费的WebVR环境。一旦使用合成数据显示出安全的可视化效果，这项工作将扩展到基于多药项目（见上文）或METADAC（一个监督获取英国5项主要研究的生物医学数据的委员会）发布的研究数据的实际用例。在这两个工作项目下开发的软件将免费提供给研究人员，帮助医生和科学家更好地分析敏感的健康数据，同时保护机密性。

The "data revolution" can enhance health/social care, accelerate research and help us to assess new ways to improve health and health-care. But new ways to analyse health data must be used in ways that the public understand, are happy with and appropriately address data privacy and security. This fellowship will develop tools to help scientists and doctors make good use of sensitive health data, while minimising the risk of an individual or their health status becoming known. I will focus on two increasingly important areas of health data use: 1) information from medical text; 2) visual display of data, particularly in augmented reality (AR) or virtual reality (VR). 1) Sensitive text analysisMedical text (eg health records, medical letters) contain patient data over time including identifying information (eg address, next of kin, full date of birth). Although helpful for care and research, use of sensitive medical text is strictly controlled for privacy reasons. Existing methods extract information from text, but may control disclosure risk by deleting identifiable data or grouping patients into blocks. But these procedures are not foolproof: some patients may still be identifiable, and after discarding key information results may be wrong. My fellowship adopts a new approach we have developed for the free software package DataSHIELD. This allows sensitive data to be analysed without being seen/copied and automatically detects and blocks many analyses that may be identifying. My earlier work has shown DataSHIELD can be used on text data and I will extend it to protect the privacy of data extracted from medical text by computer-based text mining tools. This will markedly increase the range of analyses that may be applied to medical text while maintaining confidentiality. I will first work on synthetic (made-up but realistic) text to safely develop and test the new approach. Once I am satisfied the software works, I will apply it to a research project run by Dr Sarah Slight (School of Pharmacy, Newcastle University), asking whether patients treated with many medications ("polypharmacy") have poorer outcomes (eg more falls, hospital admissions). If they do, new policies can be created to control polypharmacy and improve health outcomes. 2) Sensitive data visualisationAR/VR technologies provide a quick way to interpret and understand health data without special technical/scientific expertise. These immersive environments work because they can simultaneously present more pieces of information about someone than can be seen on paper or screen. But this also makes individuals more identifiable. If AR/VR becomes widely used, we must properly understand the disclosure risks and develop ways to protect against them. In 2015, our collaboration with industry partners Masters of Pie and Lumacode won a competition to display Wellcome Trust data in VR. Ongoing work I led extended our work to explore VR visual methods using synthetic data based on the ALSPAC cohort. Together, we built the BigDataVR pilot analysis tool. This fellowship will explore factors determining the risk of identifying someone when using immersive environments like BigDataVR. The findings will be used to develop new ways to create VR compatible graphics via DataSHIELD that convey the "essence" of a data set without full data display which may identify someone. I will create a preliminary proof of concept, using DataSHIELD to send data underpinning visualisation to the free WebVR environment. Once safe visualisation has been shown using the synthetic data, the work will be extended to a real use case based on the polypharmacy project (see above) or on research data released by METADAC (a committee overseeing access to biomedical data from 5 major UK studies). Software created under both work programs will be freely available to researchers, helping doctors and scientists to better analyse sensitive health data while protecting confidentiality.