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

描述（由申请人提供）：健康信息学中的一个未解决的问题是如何应用存储在大规模医疗记录系统中的患者过去的经验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，使得该队列的健康经历和结果对于预测是有用的。这项任务是艰巨的，因为大量的患者属性的大的可变性，随着时间的推移，患者遇到的序列的复杂性增加。流行病学方面的考虑因素，如治疗适应症的混淆也起作用。这项研究工作的目标是（1）创建一个模块化测试床，该测试床使用“大数据”系统架构，以支持对大型临床资料库的结果进行快速个性化预测的研究，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）的信息学和计算基础设施数据库（芬奇），一个拥有数千万患者记录的研究数据库，我们将探索两种协同策略，用于快速找到与索引足够相似的患者队列 患者预测近期治疗反应和/或弹性云环境中的不良反应：1）使用关键事件的时间比对，包括使用基因序列比对方法来放松对精确时间匹配的要求;以及2）使用概念距离度量来对病例记录的内容相似度进行建模。最初的应用领域将是2型糖尿病的治疗。该方法将应用开源“大数据”方法，包括Hadoop和Accumulo，以存储和过滤“医疗日志”文件。这些“日志”的内容将通过与包括使用自然语言处理工具的事件的概念标记、事件流的匹配和统计数据挖掘方法的策略相结合来处理，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）：该提案研究如何使用存储在电子病历系统中的过去患者的经验，以帮助临床医生对1型糖尿病复杂患者的护理做出实际决策。研究采用了从互联网搜索引擎和人类基因组研究中改编的方法，以确定一个病人的疾病经历与另一个病人的疾病经历相似和相关意味着什么。

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.