Development of a secure, cloud-based platform to improve record linkage & cross-agency collaboration for the public sector: using deep learning & scalable data integrations to combat the opioid crisis

开发安全的云平台以改善记录链接

• 批准号: 9622726
• 负责人: Joke Durnez
• 金额: $ 22.5万
• 依托单位: OPENLATTICE, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9622726
• 关键词: Development; secure; cloud; based; platform

Project Summary/Abstract: This SBIR Phase I proposal aims to fund research and development for a new, multitenant secure cloud-based platform specifically tailored to provide local governmental agencies with tools to share datasets and link them accurately, at high quality and low cost. The OpenLattice platform will focus on reducing drug overdoses and making drug treatment less fractured. Individual-level datasets linked across medical providers and law enforcement can support analyses of prescribing pathways and treatment trajectories that precede opioid overdose, entry into treatment, disruption, and recovery. However, linking data at the individual level has proven to be a difficult and resource-intensive endeavor compared to use of aggregate-level data, with issues with deduplication plaguing many institutional databases. With 91 American deaths recorded daily from opioid overdoses and systems of care spread across multiple institutions, the need for greater and high-quality data sharing is undeniable. Our test partner for assessing the efficacy of proposed innovations is the Greater Portland Addiction Collaborative (GPAC) in Maine, a partnership of hospitals, a police department, jail, detox treatment centers and halfway houses already working together to reduce drug overdoses. This proposal aims to demonstrate proof of concept for (i) scaling high-quality data integrations across multiple governmental domains via a standardized entity data model, and (ii) improving record linkage using neural networks. Firstly, OpenLattice is developing an open source ontology and integration scripts to standardize integration of datasets into OpenLattice's database. As the individual customization requirements decline for onboarding customers and integrating new data into the platform, costs will be greatly slashed, removing a significant barrier to data solutions for smaller counties and cities across the country, who have historically faced custom integrations, system updates, data storage fees and add-ons at high cost. The OpenLattice platform also enables use of existing ETL tools and seamless integration with police dispatch systems, emergency medical calls, healthcare records, and online prescription systems across partners who have committed to data sharing and collaboration. Secondly, OpenLattice is developing a new, proprietary algorithm for record linkage that employs a promising but as-yet commercially untested technique: a multilayer perceptron neural network, more commonly known as deep learning. In pilot research, the linking algorithm has already demonstrated success rivaling—and sometimes exceeding—current state of the art linking technologies. In Phase I, OpenLattice will continue to improve ontologies, integration tools, and the deep learning neural network, and test on publicly available datasets with dissimilar data types and formats, with manual confirmation of results. When successful, these innovations will address critical barriers to improving clinical practice in treating opioid addiction by enabling a more comprehensive continuum of care for those in treatment.

项目摘要/摘要： 该 SBIR 第一阶段提案旨在资助新的、多租户安全的基于云的研究和开发 专门为地方政府机构提供共享数据集和链接数据集的工具而定制的平台 OpenLattice 平台将专注于减少药物过量和药物过量。 减少跨医疗服务提供者和法律之间的个人层面数据集的断裂。 执法可以支持对阿片类药物之前的处方途径和治疗轨迹的分析 然而，个人层面的数据关联已成为可能。 与使用聚合级数据相比，事实证明这是一项困难且耗费资源的工作，存在问题 重复数据删除困扰着许多机构数据库，每天都会记录 91 例美国人的死亡事件。 阿片类药物过量和护理系统遍布多个机构，需要更多和高质量 数据共享是不可否认的，我们评估所提议的创新效果的测试合作伙伴是更大的。 缅因州波特兰成瘾合作组织 (GPAC)，由医院、警察局、监狱、戒毒所组成的合作组织 治疗中心和中途之家已经共同努力减少药物过量。 展示 (i) 跨多个政府部门扩展高质量数据集成的概念验证 通过标准化实体数据模型来处理域，以及 (ii) 使用神经网络改进记录链接。 首先，OpenLattice正在开发开源本体和集成脚本以标准化集成 随着个人定制要求的下降，数据集进入 OpenLattice 的数据库。 客户并将新数据集成到平台中，成本将大幅削减，从而消除大量 全国较小的县和城市的数据解决方案面临障碍，这些县和城市历来面临定制化的问题 OpenLattice 平台的集成、系统更新、数据存储费用和附加组件的成本也很高。 能够使用现有的 ETL 工具并与警察调度系统、紧急医疗系统无缝集成 致力于数据共享的合作伙伴之间的通话、医疗记录和在线处方系统 其次，OpenLattice 正在开发一种新的、专有的记录链接算法。 采用了一种很有前途但尚未经过商业测试的技术：多层感知器神经网络， 通常被称为深度学习，在试点研究中，链接算法已经得到证明。 在第一阶段，它的成功可与当前最先进的连接技术相媲美，有时甚至超过。 OpenLattice 将继续改进本体、集成工具和深度学习神经网络，并且 对具有不同数据类型和格式的公开数据集进行测试，并手动确认结果。 一旦成功，这些创新将解决改善临床治疗实践的关键障碍 通过为接受治疗的人提供更全面的连续护理来减少阿片类药物成瘾。