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

为了应对流行的医疗危机，数以千计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，这些应用程序和技术利用了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。这些应用被遗漏了一套全面的用户临床电子病历、基因组数据、与相关亚群的比较数据，以及关于对健康和生活质量重要的环境影响的数据。将这些数据整合到应用程序中存在许多障碍，主要因素是此类数据的巨大数量和异质性，其中大部分是实时流传输的，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提出，我们展望了一个健康和医疗保健的新时代，在这个时代，患者、提供者和消费者通过我们所描述的个性化人口健康的数据访问和适用性而获得授权。特别是，我们预计会有一种新的医疗保健应用程序类别，它可以从一个人的整个生活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Delphi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，从而促进广泛的健康相关软件开发人员社区更快地开发赋能的、数据驱动的健康应用程序和工具。该平台支持个人的综合“整体健康信息模型”，该模型为开发人员提供了一个单一的访问点，它(A)隐藏了分布和数据的异构性，(B)便于从这些“噪声”数据中得出推论。该平台能够根据背景和统计元数据进行新形式的分析。可伸缩性是通过理论上证明的和新提出的数据库和机器学习技术实现的。我们的研究是由三个不同的案例研究和实地试验推动的：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程序，以及区域人群健康哮喘和呼吸系统疾病的场景。Intelligence Merit Delphi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”、流式人口规模的医疗数据集上编程。此外，这些技术正在至少三个现实的现场试验中进行评估，对基于医疗“大数据”的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。布罗德影响将通过个人福祉和人口健康应用程序生态系统来展示这一点，有三个直接受益者：1)圣地亚哥灯塔社区，目前正在开发全国范围内的健康信息交换模式。2)政府和非营利机构，作为公共/私营伙伴关系的典范，促进全社区的健康。3)私营行业，在这里是Qualcomm Life的/2net平台，在这里我们演示了如何以新的方式利用现有服务来处理健康数据。最后，该项目将作为研究生、博士后和住院医生的个性化人口健康培训基地。

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.