Using the literature to build causal models of retrospective observational data

利用文献建立回顾性观察数据的因果模型

• 批准号: 10444995
• 负责人: Scott Alexander Malec
• 金额: $ 6.87万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10444995
• 关键词: Address; Adverse drug event; Adverse event; Age; Aging; Alcoholism; Algorithms; Alzheimer' s Disease; Benchmarking; Characteristics; Chronic; Controlled Clinical Trials; Cross-Sectional Studies; Data; Data Set; Dependence; Diagnosis; Disease; Drug Exposure; Drug Side Effects; Drug usage; Effectiveness; Electronic Health Record; Etiology; Event; Health; High Prevalence; Knowledge; Light; Link; Literature; Manuals; Medical; Medical center; Mentors; Methods; Naltrexone; Neurodegenerative Disorders; Outcome; Pancreatitis; Partner in relationship; Patients; Peer Review; Performance; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacology; Phase; Prevalence; Reference Standards; Research; Research Personnel; Reverse engineering; Review Literature; Safety; Selection for Treatments; Structure; Symptoms; System; Testing; Translational Research; Variant; Work; base; causal model; comorbidity; data quality; drug repurposing; guided inquiry; health application; health data; improved; insight; medication safety; pharmacovigilance; prevent; side effect

Health data contain a wealth of information for research. Health data, such as found in electronic health records (EHRs), allow for the identification links between health events, such as drug exposures and side- effects. Some of these links indicate stable dependencies deemed as causes. Causal insight allows reverse- engineering disease. If confounding is not addressed, it will be difficult to distinguish causative from correlative links. Our approach is to identify confounders explicitly. Graphical causal modeling (GCMs) can discover causal links from data and prior knowledge. GCMs summarize causal links between variables. Automated selection of variables would allow GCMs to scale and yield more insight from data. Literature-based discovery (LBD) methods were developed to identify links between concepts in the literature. Advanced methods permit the search for concepts linked to each other through specific verbs, e.g., “causes”, “treats”. Our hypothesis is that we can exploit structured knowledge extracted from the literature to inform GCMs. In prior work, we found that LBD + GCM was better at identifying side-effects in EHR data than traditional methods. Compared to methods which use solely data, we hypothesize that our method will increase the ability to detect causal relationships from EHR data. The first aim is to determine the extent to which LBD-informed GCM improves the identification of causal links for drug safety. We will build LBD-informed GCMs using publicly available reference datasets for drug safety. These reference datasets contain drug/side-effect pairs for performance benchmarking. (A) Test the ability of GCM algorithms to identify known causal links solely using data. We will systematically evaluate GCM algorithms based on their ability to re-discover causal links in a reference standard. Results will guide our studies on how GCM can be tuned. (B) Determine the effect of adding different subsets of LBD-derived information to GCMs at identifying drug side-effects. We will build causal models using increasing numbers confounders. The second aim is to test the ability of LBD built with disease-specific literature to improve the relevance of LBD derived confounders for Alzheimer's Disease (AD). We chose AD for its high prevalence and relative lack of effective pharmacologic treatment. (A) Compare LBD strategies in a disease-specific setting. We will test LBD variants using disease-specific literature or with LBD lacking subject- matter restrictions. (B) Define the ability of robust LBD-informed GCM to validate drug repurposing candidates for treating AD symptoms. We will test the ability of advanced methods to iteratively resolve hidden latent confounding, when detected, to improve effect estimates. The fulfillment of these aims will yield new methods to combine insights from the literature with causal modeling to uncover causal relationships of drug exposures on adverse events and on beneficial outcomes.

健康数据包含大量的研究信息。健康数据，例如电子健康 记录（EHR），允许健康事件之间的识别联系，例如药物暴露和侧面 效果。其中一些链接表明被认为是原因的稳定依赖关系。因果见解允许反向 工程疾病。如果没有解决混淆，将很难区分谨慎与相关性 链接。我们的方法是明确识别混杂因素。图形因果建模（GCM）可以发现 来自数据和先验知识的因果链接。 GCMS摘要变量之间的因果关系。自动化 选择变量将使GCM从数据扩展并产生更多的见解。基于文学的发现 开发了（LBD）方法以识别文献中概念之间的联系。高级方法允许 搜索通过特定动词，例如“原因”，“ Treats”相互关联的概念。我们的假设是 我们可以探索从文献中提取的结构化知识，以告知GCMS。在先前的工作中，我们发现 与传统方法相比，LBD + GCM在识别EHR数据中的副作用方面更好。相比 仅使用数据的方法，我们假设我们的方法将增加检测催化的能力 来自EHR数据的关系。第一个目的是确定LBD信息的GCM改善的程度 确定药物安全因果关系。我们将使用公开可用 毒品安全的参考数据集。这些参考数据集包含用于性能的药物/副作用对 基准测试。 （a）测试GCM算法仅使用数据识别已知因果链接的能力。我们将 系统地评估GCM算法基于其重新发现毒品链接的能力 标准。结果将指导我们如何调整GCM的研究。 （b）确定添加不同的效果 LBD衍生的信息的子集向GCMS识别药物副作用。我们将使用 越来越多的人数混淆。第二个目的是测试具有特异性疾病的LBD的能力 文献提高了LBD衍生的混杂因素与阿尔茨海默氏病（AD）的相关性。我们选择广告 其高流行率和相对缺乏有效的药物结肠治疗。 （a）比较一个LBD策略 特定疾病的环境。我们将使用疾病特异性文献或缺乏受试者的LBD测试LBD变体。 物质限制。 （b）定义强大的LBD信息GCM验证药物重新利用候选者的能力 用于治疗广告症状。我们将测试高级方法的能力迭代解决隐藏的潜在 在检测到时，混淆以改善效应估计。这些目标的实现将产生新的方法 将文献中的见解与因果建模结合在一起，以发现药物暴露的因果关系 关于不利事件和有益结果。