Decentralized differentially-private methods for dynamic data release and analysis

项目摘要 大数据集在开发和评估人工智能(AI)和 用于预测发病率、死亡率和其他重要健康结果的统计学习模型。 医疗机构是患者数据的管理者，并希望为 预测分析工具的开发、评估和利用。然而，他们也知道 根据HIPAA的规则，这种简单的“取消身份识别”不足以保护患者的隐私。 此外，保护市场份额、缺乏对谁使用的控制等其他因素 共享数据的目的，以及对患者对共享数据的反应的担忧 在没有明确同意的情况下，发起某些注册中心和集中存储库等活动 难以实施。我们已经证明了分解算法是可能的，以便它们 可以使用驻留在每个医疗保健中心的数据运行，从而减轻了对控制的担忧 以及潜在的误用。在这个项目的第一阶段，我们集中展示了 这些算法在慢性病研究中的准确性和性能(1) 获得关于这种情况的新知识是缓慢的(即，对这种疾病已经很好地了解了，所以 科学发现没有迅速公布)；和(2)发病率和 这种疾病的表现在不同的地方和不同的人之间没有明显的差异。 人。在这次竞争更新中，我们建议开发分散的预测模型，以 满足慢性病的所有要求，但这些方法也适用于快速发展的 新冠肺炎等急性情况。我们提出了新的方法来处理可能被 缺少某些患者简档或某些变量，但仍可参与模型学习， 评估和实施。这些新的人工智能算法将允许监督和非监督 跨机构学习，使用来自多种模式的数据(例如，成像、基因组、 实验室测试)，并将允许隐私保护记录链接。我们将测试这些算法 来自美国三个高度多样化的医疗中心的数据和方法：埃默里 亚特兰大大学、休斯顿德克萨斯大学健康科学中心和德克萨斯大学 加利福尼亚州，圣地亚哥。

Project Summary Large data sets are important in the development and evaluation of artificial intelligence (AI) and statistical learning models to predict morbidity, mortality, and other important health outcomes. Healthcare institutions are stewards of their patients’ data, and want to contribute to the development, evaluation, and utilization of predictive analytics tools. However, they also know that simple “de-identification” per HIPAA rules is not sufficient to protect patient privacy. Additionally, other factors such as protection of market share, lack of control about who uses shared data for what purposes, and concerns about patients’ reactions to having their data shared without explicit consent make initiatives such as certain registries and centralized repositories difficult to implement. We have shown that it is possible to decompose algorithms so that they can run on data that stays at each healthcare center, thus mitigating the concerns about control and potential misuse. In the first phase of this project, we concentrated on demonstrating the accuracy and performance of these algorithms for the study of chronic diseases in which (1) acquisition of new knowledge about the condition is slow (i.e., the disease is well understood, so scientific discoveries are not being published at a rapid pace); and (2) the incidence and presentation of the disease do not vary dramatically from place to place, and from person to person. In this competitive renewal, we propose to develop decentralized predictive models that meet all requirements for chronic diseases, but the methods are also applicable to rapidly evolving acute conditions such as COVID-19. We propose new approaches to deal with sites that may be missing certain patient profiles or certain variables but can still participate in model learning, evaluation and implementation. These new AI algorithms will permit supervised and unsupervised learning across institutions, using data from multiple modalities (e.g., imaging, genomes, laboratory tests), and will allow privacy-protecting record linkage. We will test these algorithms and approaches in data from three highly diverse medical centers across the US: Emory University in Atlanta, University of Texas Health Science Center at Houston, and University of California, San Diego.