BIGDATA: Mid-Scale: DA: Techniques to Integrate Disparate Data: Clinical Personalized Pragmatic Predictions of Outcomes (C3PO)

BIGDATA：中等规模：DA：整合不同数据的技术：临床个性化实用结果预测 (C3PO)

• 批准号: 8914880
• 负责人: Lewis James Frey
• 金额: $ 50.57万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8914880
• 关键词: BIGDATA; Mid; Scale; DA; Techniques

DESCRIPTION (provided by applicant): An unsolved problem in health informatics is how to apply the past experiences of patients, stored in large-scale medical records systems, to predict the outcomes of patients and to individualize care. One approach to prediction, heretofore impractical, is rapidly finding a patient cohort "similar enough" to an index case that the health experiences and outcomes of this cohort are informative for prediction. This task is formidable because of large variability of the vast numbers of patient attributes with the added complexity of sequences of patient encounters evolving over time. Epidemiological considerations such as confounding by indication for treatment also come into play. The objective of this research effort is to (1) create a modular test bed that uses a "big data" systems architecture to support research in rapid individualized prediction of outcomes from large clinical repositories and (2) to explore various approaches to making "pragmatic" near-term predictions of outcomes. Using the Department of Veterans Affairs' (VA) Informatics and Computing Infrastructure database (VINCI), a research database with records of tens of millions of patients, we will explore two synergistic strategies for rapidly finding a cohort of patients that are similar enough to an index patient to predict near-term treatment response and/or adverse effects in an elastic cloud environment: 1) use of temporal alignment of critical events including use of gene sequence alignment methods to relax requirements for exact temporal matching; and, 2) use of conceptual distance metrics to model the degree of content similarity of case records. The initial domain of application will be treatment of Type 2 diabetes. The approach will apply open source "big data" methodologies, including Hadoop and Accumulo, to store and filter "medical log" files. The content of these "logs" will be processed by a combination with strategies including conceptual markup of events using natural language processing tools, matching of event streams, and statistical data mining methods to rapidly retrieve and identify patients that are sufficiently similar to an index case to be able to make personalized yet pragmatic clinical predictions of outcomes. RELEVANCE (See instructions): This proposal studies how to use experience of past patients, stored in electronic medical records systems, to help clinicians make practical decisions on the care of complex patients with type 1 diabetes. Research applies methods adapted from Internet search engines and from studies of the human genome to determine what it means for one patient's disease experiences to be similar to and relevant to another's.

描述（由申请人提供）：健康信息学中一个未解决的问题是如何应用存储在大型医疗记录系统中的患者过去的经历来预测患者的结果并进行个性化护理。迄今为止不切实际的一种预测方法是快速找到与索引病例“足够相似”的患者队列，该队列的健康经历和结果可为预测提供信息。这项任务非常艰巨，因为大量患者属性存在很大的可变性，并且随着时间的推移，患者就诊顺序也变得更加复杂。流行病学方面的考虑，例如治疗适应症的混淆，也发挥了作用。这项研究工作的目标是（1）创建一个模块化测试台，使用“大数据”系统架构来支持大型临床存储库结果的快速个性化预测研究，以及（2） 探索各种方法来对结果进行“务实”的近期预测。使用退伍军人事务部 (VA) 信息学和计算基础设施数据库 (VINCI)（一个包含数千万患者记录的研究数据库），我们将探索两种协同策略，以快速找到与索引足够相似的患者队列 患者在弹性云环境中预测近期治疗反应和/或不良反应：1）使用关键事件的时间对齐，包括使用基因序列对齐方法来放宽对精确时间匹配的要求； 2）使用概念距离度量对案例记录的内容相似度进行建模。最初的应用领域是治疗 2 型糖尿病。该方法将应用开源“大数据”方法，包括 Hadoop 和 Accumulo，来存储和过滤“医疗日志”文件。这些“日志”的内容将通过结合策略进行处理，包括使用自然语言处理工具对事件进行概念标记、事件流匹配和统计数据挖掘方法，以快速检索和识别与索引病例足够相似的患者，从而能够对结果进行个性化但实用的临床预测。相关性（参见说明）：本提案研究如何利用电子病历系统中存储的过去患者的经验来帮助临床医生就复杂的 1 型糖尿病患者的护理做出实际决策。研究应用改编自互联网搜索引擎和人类基因组研究的方法来确定一名患者的疾病经历与另一名患者的疾病经历相似和相关意味着什么。