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

“数据革命”可以加强卫生/社会保健，加快研究，并帮助我们评估改善卫生和卫生保健的新方法。但是，分析健康数据的新方法必须以公众理解、满意并适当解决数据隐私和安全的方式使用。该奖学金将开发工具，帮助科学家和医生很好地利用敏感的健康数据，同时将个人或他们的健康状况被知道的风险降至最低。我将集中讨论健康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、医疗信函)包含一段时间内的患者数据，包括识别信息(如地址、近亲、完整出生日期)。尽管有助于护理和研究，但出于隐私原因，对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，但可以通过删除可识别的数据或将患者分组来控制泄漏风险。但这些程序并不是万无一失的：一些患者可能仍然可以识别，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自由软件包DataSHIELD开发的一种新方法。这允许在不被查看/复制的情况下分析敏感数据，并自动检测和阻止许多可能正在识别的分析。我以前的工作表明，DataSHIELD可以用于文本数据，我将对其进行扩展，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。这将显著增加可应用于医学文本的分析范围，同时保持机密性。我将首先处理合成的(虚构的但真实的)文本，以安全地开发和测试新方法。一旦我对该软件的运行感到满意，我将把它应用到由纽卡斯尔大学药学院的萨拉·斯莱姆博士主持的一个研究项目中，该项目询问接受多种药物治疗的患者是否有更差的结果(例如更多的跌倒、住院)。如果他们这样做了，就可以制定新的政策来控制多药制并改善健康结果。2)敏感数据可视化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。这些身临其境的环境之所以有效，是因为它们可以同时呈现比在纸上或屏幕上看到的更多的关于某人的信息。但这也让个人更容易被识别。如果AR/VR得到广泛应用，我们必须正确认识披露风险，并制定防范方法。2015年，我们与行业合作伙伴Master of Pie和Lumacode合作，赢得了在VR中展示Wellcome Trust数据的比赛。我领导的正在进行的工作扩展了我们的工作，使用基于ALSPAC队列的合成数据来探索VR视觉方法。我们一起构建了BigDataVR试点分析工具。这项研究将探索在使用像BigDataVR这样的沉浸式环境时，决定识别某人的风险的因素。这些发现将被用来开发通过DataSHIELD创建与VR兼容的图形的新方法，这些图形传达了数据集的“本质”，而不是完整的数据显示，可能会识别某人。我将创建一个初步的概念验证，使用DataSHIELD将支持可视化的数据发送到免费的WebVR环境。一旦使用合成数据显示了安全的可视化，这项工作将扩展到基于综合药房项目(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)发布的研究数据的真实用例。根据这两个工作计划创建的软件将免费提供给研究人员，帮助医生和科学家在保护机密性的同时更好地分析敏感的健康数据。

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.