Integrated discovery and hypothesis testing of new associations in rare diseases

罕见疾病新关联的综合发现和假设检验

• 批准号: 7727710
• 负责人: Raul Rabadan
• 金额: $ 53.3万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7727710
• 关键词: Acquired Immunodeficiency Syndrome; Affect; Case Study; Cells; Clinical; Code; Computer software; Curiosities; Data; Data Set; Databases; Disease; Electronic Health Record; Electronics; Environmental Risk Factor; Evaluation; Frequencies; Goals; Hand; Hospitals; Immunocompromised Host; Incidence; Individual; Inequality; Informatics; Information Theory; Kaposi Sarcoma; Kidney Diseases; Laboratories; Link; Literature; Liver diseases; Methodology; Methods; Mining; Natural Language Processing; Nature; New York; Patients; Pattern; Persons; Population; Presbyterian Church; Process; PubMed; Rare Diseases; Records; Reporting; Research; Source; Statistical Methods; Stratification; Stream; Stress; System; Techniques; Testing; Text; Transplantation; Unified Medical Language System; United States National Institutes of Health; United States National Library of Medicine; Virus; Work; Writing; abstracting; base; blind; data mining; forgetting; improved; innovation; interdisciplinary approach; longitudinal database; novel; pathogen; repository; research study; statistics; text searching; tool; virology; web site

DESCRIPTION (provided by applicant): Rare diseases are studied in isolated laboratories, forgotten by main stream pharmacological companies, and considered almost academic curiosities. Finding variables that correlate/cause rare diseases (a condition is rare when it affects less than 1 person per 2,000) is a difficult task. The low number of cases and the sparse nature of the reports make it difficult to obtain significant/meaningful statistical results. There are two ways to avoid these problems. The first is to integrate reported cases and associations to generate enough statistical power. The second way is to have an independent data set, big enough to cover rare cases. Each of the two methods has intrinsic problems. For instance, the search in the literature puts together different studies, each of them with their own biases in population, methodology and objectives. On the other hand, blind searches for associations in big databases introduce a large number of false positives due to multiple hypothesis testing. These problems could be avoided by developing innovative methods that allow the integration of information and methodologies in the literature and longitudinal databases. To achieve this goal, we propose a team that combines expertise in natural language processing systems (Carol Friedman), electronic health records (George Hripcsak), statistics in combined databases and computational virology (Raul Rabadan). This team will generate an interdisciplinary approach to mine and integrate the literature and the dataset collected at Columbia/New York Presbyterian hospital. Identifying unusual correlations in rare diseases is the first step to understanding the origin of the diseases and to finding a cure for them. We hypothesize that we will develop effective methods aimed at improving our understanding of rare diseases by combining hypothesis testing and hypothesis discovery, and by integrating information from the literature and from the patient record to obtain increased statistical power. This will involve using natural language processing and statistical methods to mine both the literature and the electronic health record (EHR).

描述（由申请人提供）：罕见疾病在孤立的实验室中进行研究，被主流药理学公司遗忘，几乎被视为学术好奇心。寻找与罕见疾病相关/导致罕见疾病的变量（当每2,000人中影响不到1人时，这种情况是罕见的）是一项艰巨的任务。病例数量少，报告稀少，因此难以获得有意义/有意义的统计结果。有两种方法可以避免这些问题。第一是整合报告的案例和关联，以产生足够的统计效力。第二种方法是拥有一个独立的数据集，足够大，以涵盖罕见的病例。这两种方法都有其内在的问题。例如，文献检索将不同的研究放在一起，每个研究在人口，方法和目标方面都有自己的偏见。另一方面，在大数据库中盲目搜索关联会由于多个假设检验而引入大量假阳性。 这些问题可以通过开发创新的方法来避免，这些方法可以整合文献和纵向数据库中的信息和方法。为了实现这一目标，我们提出了一个团队，结合自然语言处理系统（卡罗尔弗里德曼），电子健康记录（乔治Hripcsak），统计数据库和计算病毒学（劳尔拉巴丹）的专业知识。该团队将采用跨学科的方法来挖掘和整合哥伦比亚/纽约长老会医院收集的文献和数据集。识别罕见疾病中的异常相关性是了解疾病起源并找到治愈方法的第一步。我们假设，我们将开发有效的方法，旨在提高我们对罕见疾病的理解，通过结合假设检验和假设发现，并通过整合文献和病历中的信息，以获得更高的统计功效。这将涉及使用自然语言处理和统计方法来挖掘文献和电子健康记录（EHR）。