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: 8840825
• 负责人: Lewis James Frey
• 金额: $ 52.95万
• 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/8840825
• 关键词: Address; Adoption; Adverse effects; Algorithms; Beds; Benchmarking; Big Data; Biological; Biological Models; Biosensing Techniques; Budgets; Caring; Cataloging; Catalogs; Centers for Disease Control and Prevention (U.S.); Child; Child health care; Childhood; Classification; Clinic Visits; Clinical; Clinical Data; Clinical Trials; Cluster Analysis; Collaborations; Complex; Computer Systems; Computerized Medical Record; Coupled; Critical Care; Data; Data Analyses; Data Element; Data Set; Databases; Development; Disasters; Disease; Environment; Epidemiology; Event; Exclusion; Extensible Markup Language; Funding; Gene Expression; Genes; Genetic; Genome; Genomics; Germ-Line Mutation; Goals; Health; Health system; Human Development; Human Genome; Imagery; Informatics; Information Systems; Institutes; Instruction; Insulin-Dependent Diabetes Mellitus; Internet; Language; Letters; Location; Logical Observation Identifiers Names and Codes; Machine Learning; Malignant Neoplasms; Measures; Medical; Medical Records; Medicine; Metadata; Methodology; Methods; Modeling; Mutation; Names; Natural Language Processing; Non-Insulin-Dependent Diabetes Mellitus; Oncogenes; Ontology; Outcome; Patient Care; Patients; Pattern; Performance; Play; Privacy; Process; Public Health Informatics; Records; Relative (related person); Reporting; Research; Research Infrastructure; Research Personnel; Research Project Grants; Research Support; Resources; Role; Sequence Alignment; Somatic Mutation; Source; Specialist; Stream; Structure; System; Techniques; Technology; Terminology; Testing; Text; Time; Translational Research; Triage; United States National Library of Medicine; Variant; Veterans; Visualization software; Vocabulary; Wireless Technology; Work; base; bench to bedside; cancer type; clinical care; clinical practice; cohort; data integration; data mining; data visualization; design; emergency service responder; experience; genetic variant; genome analysis; genome sequencing; improved; indexing; interoperability; medical information system; novel; open source; parallel processing; performance tests; processing speed; repository; research study; response; sugar; system architecture; tool; treatment response; tumor; tumor progression; virtual

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）：健康信息学中的一个未解决的问题是如何运用存储在大规模病历系统中的患者的过去经历，以预测患者的结果并个性化护理。迄今为止不切实际的一种预测方法正在迅速发现患者队列与指数案例“足够相似”，以至于该队列的健康经历和结果可用于预测。由于大量患者属性的差异很大，随着时间的流逝，患者遇到的序列序列增加了复杂性，因此此任务是巨大的。流行病学考虑因素，例如通过治疗的指示混淆也发挥了作用。这项研究工作的目的是（1）创建一个模块化测试床，该床使用“大数据”系统体系结构来支持对大型临床存储库结果的快速个性化预测，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），这是一个研究数据库，具有数千万患者的记录，我们将探索两种协同策略，以迅速找到与索引足够相似的患者 患者可以在弹性云环境中预测近期治疗反应和/或不良反应：1）使用时间对齐的关键事件的时间对齐，包括使用基因序列比对方法放松确切时间匹配的要求； 2）使用概念距离指标来建模案例记​​录的内容相似性的程度。应用的最初领域将是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”的内容将通过结合策略来处理，包括使用自然语言处理工具对事件进行概念标记，事件流的匹配以及统计数据挖掘方法，以迅速检索和识别与索引案例足够相似的患者，以便能够实现个性化而实用的务实临床预测。相关性（请参阅说明）：该提案研究如何利用过去患者的经验（存储在电子病历系统中），以帮助临床医生对1型糖尿病的复杂患者的护理做出实际决定。研究适用于互联网搜索引擎和人类基因组研究的适用方法，以确定与他人相似并与他人相似并相关的意义。