Integrated discovery and hypothesis testing of new associations in rare diseases

描述(申请人提供)：罕见疾病是在隔离的实验室中研究的，被主流药学公司遗忘，几乎被认为是学术上的新奇事物。寻找与罕见疾病相关/导致罕见疾病的变量是一项艰巨的任务(当一种疾病的发病率低于每2000人1人时，这种疾病就很罕见)。案件数量少，报告稀少，很难获得重大/有意义的统计结果。有两种方法可以避免这些问题。第一是整合上报的病例和关联，以产生足够的统计力量。第二种方法是拥有一个独立的数据集，其大小足以涵盖罕见病例。这两种方法都有内在的问题。例如，在文献中的搜索将不同的研究组合在一起，每一项研究在人口、方法和目标方面都有自己的偏见。另一方面，由于多重假设检验，在大型数据库中对关联进行盲目搜索会导致大量的误报。 这些问题可以通过开发创新的方法来避免，这些方法可以将文献和纵向数据库中的信息和方法结合起来。为了实现这一目标，我们建议成立一个团队，将自然语言处理系统(Carol Friedman)、电子健康记录(George Hlipcsak)、组合数据库中的统计和计算病毒学(Raul Rabadan)方面的专业知识结合起来。这个团队将产生一个跨学科的方法来挖掘，并整合文献和在哥伦比亚大学/纽约长老会医院收集的数据集。识别罕见疾病中的异常相关性是了解这些疾病的起源并找到治疗方法的第一步。我们假设，我们将开发有效的方法，旨在通过结合假设检验和假设发现，以及通过整合文献和患者记录中的信息来提高我们对罕见疾病的理解，以获得更大的统计能力。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)。

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).