Integrated discovery and hypothesis testing of new associations in rare diseases

描述（由申请人提供）：罕见病是在孤立的实验室中研究的，被主流药理学公司所遗忘，几乎被认为是学术界的奇闻。寻找与罕见病相关/引起罕见病的变量是一项艰巨的任务（当一种疾病每2000人中影响不到1人时，它就是罕见病）。病例数少，报告稀疏，难以获得显著/有意义的统计结果。有两种方法可以避免这些问题。首先是整合报道的案例和关联，以产生足够的统计力量。第二种方法是有一个独立的数据集，足够大以涵盖罕见的情况。这两种方法都有其内在的问题。例如，文献中的搜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，在大型数据库中盲目搜索关联会由于多重假设检验而导致大量误报。

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).