SHB: Type II (INT): DELPHI: Data E-platform Leveraged for Patient Empowerment and Population Health Improvement

为了应对流行病的医疗危机，成千上万的软件开发人员一直在创新新的个人医疗保健应用程序和技术，利用医疗和计算技术的进步。 尽管这些工具处理的个人数据源源不断--体重、活动、饮食、心率等--但它们的数据相对较少。 这些应用程序中没有包括用户的临床电子医疗记录、基因组数据、相关亚群的比较数据以及对健康和生活质量有重要影响的环境影响数据等一系列综合数据。将这些数据纳入应用程序中存在许多障碍，主要因素是这些数据的巨大数量和异质性，其中大部分是实时流媒体，并分布在不同的利益相关者平台上。 一个相关的问题是从这些数据中得出推论。随着数据库和机器学习的进步，我们设想了一个健康和医疗保健的新时代，在这个时代，患者、提供者和消费者都能获得数据访问和适用性，我们将其描述为个性化的人群健康。 特别是，我们期待一种新的医疗保健应用类别，从一个人的整个生命历史和背景的角度来推断一个人的健康状况，并帮助执行干预措施。该项目正在进行基础和应用研究，以支持一个名为德尔菲的平台，该平台可以集成访问和分析所有与健康相关的数据，从而促进赋权的更快速发展，数据驱动的健康应用程序和工具，由一个广泛的社区健康相关的软件开发人员。该平台支持个人的集成“整体健康信息模型”，该模型为开发人员提供了单一访问点，该访问点（a）隐藏分布和数据异质性，并且（B）便于从这些“噪声”数据中得出推论。该平台支持基于上下文和统计元数据的新型分析形式。 可扩展性是通过理论上证明和新提出的数据库和机器学习技术实现的。 我们的研究由三个不同的案例研究和现场试验驱动：面向临床医生的1型糖尿病干预，面向患者和消费者的高血压应用，智力优势德尔菲在数据库和机器学习方面取得了根本性的进步，使广泛的程序员社区-从全职专业人员到相对新手-在“实时”、流媒体人口规模的医疗数据集上进行编程。 此外，这些技术正在至少三个真实的现场试验中进行评估，从而对医疗“大数据”计算的性质以及我们提出的使其易于处理的技术产生了新的见解。更广泛的影响这将通过个人福祉和人口健康应用生态系统来证明，其中有三个直接受益者：1）圣地亚哥灯塔社区，目前正在全国范围内开发的卫生信息交流模式。2)政府和非营利机构作为公私伙伴关系的典范，促进全社区的健康。3)私营行业，在这种情况下，高通生命的/2net平台，我们展示了如何利用现有的服务，以新颖的方式来处理健康数据。最后，该项目将作为研究生、博士后和住院医师个性化人群健康的培训基地。

In response to a healthcare crisis of epidemic proportions, thousands of software developers have been innovating new personal healthcare applications and technologies that leverage advances in medical and computing technology. Despite the endless streams of personal data that these tools process -- weight, activity, diet, heart rate, etc. -- they are relatively data poor. Left out of these applications is a comprehensive set of users' clinical electronic medical records, genomic data, comparative data with relevant subpopulations, and data on environmental influences important to health and quality of life.There are numerous barriers to incorporating such data in applications, the dominant factors being the tremendous volume and heterogeneity of such data, much of it streaming in real-time and spread across disparate stakeholder platforms. A related problem is drawing inferences from these data. With the advances in databases and machine learning proposed, we envision a new era of health and healthcare where patients, providers and consumers are empowered by data access and applicability that we characterize as personalized population health. In particular, we anticipate a new category of healthcare applications that infer one's health status - and help execute interventions - in the perspective of one's entire life history and context.This project is conducting fundamental and applied research in support of a platform, called DELPHI, that enables integrated access and analysis of all data relevant to health, and consequently promotes more rapid development of empowering, data-driven health apps and tools by a broad community of health-related software developers. The platform supports an integrated "whole health information model" of the individual that provides developers a single point of access that both (a) hides distribution and data heterogeneities, and (b) facilitates drawing inferences from these "noisy" data. The platform enables novel forms of analyses based on contextual and statistical metadata. Scalability is achieved through theoretically proven and newly proposed database and machine learning techniques. Our research is driven by three disparate case studies and field trials: a clinician-facing type-1 diabetes intervention, a patient and consumer-facing hypertension application, and a regional population health asthma and respiratory disease scenario.Intellectual Merit DELPHI is yielding fundamental advances in databases and machine learning that enable a wide community of programmers - from full-time professional to relative novices - to program on top of a "live", streaming population-scale medical dataset. Additionally, these techniques are being evaluated in at least three realistic field trials, yielding new insights on both the nature of computing on medical "big data" and the techniques we have proposed to make it tractable.Broader ImpactThis will be demonstrated through a personal well-being and population health applications ecosystem, with three immediate beneficiaries: 1) The San Diego Beacon Community, a model for health information exchanges currently under development nationally. 2) Governmental and non-profit agencies who serve as an example of public/private partnerships to promote community-wide health. 3) Private industry, in this case Qualcomm Life's/2net platform where we demonstrate how to utilize existing services in novel ways to handle health data. Finally, this project will serve as a training ground in personalized population health for graduate students, post docs and medical residents.