Secure Sharing of Clinical History & Genetic Data: Empowering Predictive Pers. Me

描述(由申请人提供)： 计算机辅助医学正处于十字路口：医疗保健需要准确的数据，但广泛提供此类数据可能会给个别患者的隐私带来不可接受的风险。这种实用性和隐私性之间的紧张关系在预测性个性化医学(PPM)中尤为尖锐。PPM承诺根据个人的特定遗传学和临床病史做出量身定做的治疗决定。要使PPM成为现实，需要对组合的遗传、临床和人口数据运行统计、数据挖掘和机器学习算法，以构建预测模型。对此类数据的访问直接与医疗保健提供者保护每个患者数据隐私的需求相竞争，因此在模型有效性和隐私之间产生了权衡。因此，我们发现自己陷入了不幸的僵局：代表数据集中代表患者的重大隐私担忧，阻碍了更多不同研究人员对数据进行更有力挖掘所带来的重大医学进步。在这项拟议的工作中，我们寻求开发和评估技术来解决这一僵局，使卫生从业者和研究人员能够对隐私敏感的医疗记录进行计算，以便在保护患者隐私的同时做出治疗决策或创建准确的模型。我们将对我们的方法进行评估，以识别的实际电子病历为基础，每个患者的平均临床病史为29年，并使用5000名患者子集的详细基因数据(650K SNPs)。我们现在可以通过威斯康星州基因组计划获得这个数据集，但只能在马什菲尔德诊所的计算机上获得。如果成功，我们的方法将使共享这一尖端数据集以及其他正在开发中的类似数据集成为可能，包括我们在UW-Madison分析这些数据的能力，我们的秃鹰池中有数千个处理器可用。我们的隐私方法集成了安全的数据访问环境，包括适合使用笔记本电脑和云计算的环境，以及新颖的匿名算法，为数据和/或发布的数据分析结果提供不同的隐私保证。为此，我们的具体目标如下： 目标1：开发和部署一个安全的本地环境，与安全的网络功能相结合，将确保临床机构和研究人员之间共享的电子病历和生物医学数据集的端到端安全和隐私。 目标2：开发和部署一个安全的虚拟环境，以便在“云中”进行大规模的、保护隐私的数据分析。 目标3：开发和评估隐私保护数据挖掘算法，用于由电子病历和基因数据组成的原始(非匿名)数据集。 目标4：开发和评估适用于包含基因数据的电子病历中存在的复杂结构的匿名数据发布算法和隐私保证。

DESCRIPTION (provided by applicant): Computer-assisted medicine is at a crossroads: medical care requires accurate data, but making such data widely available can create unacceptable risks to the privacy of individual patients. This tension between utility and privacy is especially acute in predictive personalized medicine (PPM). PPM holds the promise of making treatment decisions tailored to the individual based on her or his particular genetics and clinical history. Making PPM a reality requires running statistical, data mining and machine learning algorithms on combined genetic, clinical and demographic data to construct predictive models. Access to such data directly competes with the need for healthcare providers to protect the privacy of each patient's data, thus creating a tradeoff between model efficacy and privacy. Thus we find ourselves in an unfortunate standoff: significant medical advances that would result from more powerful mining of the data by a wider variety of researchers are hindered by significant privacy concerns on behalf of the patients represented in the data set. In this proposed work, we seek to develop and evaluate technology to resolve this standoff, enabling health practitioners and researchers to compute on privacy-sensitive medical records in order to make treatment decisions or create accurate models, while protecting patient privacy. We will evaluate our approach on a de-identified actual electronic medical record, with an average of 29 years of clinical history on each patient, and with detailed genetic data (650K SNPs) available for a subset of 5000 of the patients. This data set is available to us now through the Wisconsin Genomics Initiative, but only on a computer at the Marshfield Clinic. If successful our approach will make possible the sharing of this cutting-edge data set, and others like it that are now in development, including our ability to analyze this data at UW-Madison where we have thousands of processors available in our Condor pool. Our privacy approach integrates secure data access environments, including those appropriate to the use of laptops and cloud computing, with novel anonymization algorithms providing differential privacy guarantees for data and/or published results of data analysis. To this end, our specific aims are as follows: AIM 1: Develop and deploy a secure local environment that, in combination with secure network functionality, will ensure end-to-end security and privacy for electronic medical records and biomedical datasets shared between clinical institutions and researchers. AIM 2: Develop and deploy a secure virtual environment to allow large-scale, privacy-preserving data analysis "in the cloud." AIM 3: Develop and evaluate privacy-preserving data mining algorithms for use with original (not anonymized) data sets consisting of electronic medical records and genetic data. AIM 4: Develop and evaluate anonymizing data publishing algorithms and privacy guarantees that are appropriate to the complex structure present in electronic medical records with genetic data.