An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions

用于发现和确定药物补充剂相互作用的信息学框架

• 批准号: 10119590
• 负责人: RUI ZHANG
• 金额: $ 33.93万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10119590
• 关键词: Address; Administrative Supplement; Adult; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Artificial Intelligence; Award; Big Data; Clinic; Clinical; Clinical Research; Communities; Complement; Computerized Medical Record; Consumption; Data; Data Analytics; Data Set; Data Sources; Dementia; Detection; Development; Elderly; Electronic Health Record; Electronic Medical Records and Genomics Network; Florida; Future; Genomics; Healthcare; Healthcare Systems; Human; Impaired cognition; Individual; Informatics; Institution; Intake; Investigation; Letters; Link; Literature; Methods; Mining; Natural Language Processing; Outcome; Parents; Patients; Pharmaceutical Preparations; Records; Research; Role; Safety; Sample Size; Signal Transduction; Surveys; Terminology; Training; Universities; Vascular Dementia; Work; analytical method; base; cohort; deep learning; dietary supplements; evidence base; genomic data; improved; innovation; interest; learning strategy; mild cognitive impairment; novel; pharmacovigilance; prevent; response; treatment response

PROJECT SUMMARY Most U.S. adults (68%) take dietary supplements and there is increasing evidence of drug-supplement interactions (DSIs); In recent years, there has been increasing evidence supporting the role of DSs in ADRD in preventing cognitive impairment but there is limited evidence and the sample sizes have been small. Real- world data (RWD) especially the EHR contain detailed treatment and response information from patients and could be used to detect the usage and effect of DSs, DSIs, which is more translational to clinical outcomes (e.g., MCI to ADRD conversion). To the best of our knowledge, there is no investigation on DSs usage and safety among patients in MCI and ADRD using EHR data. Our current parent award is focusing on the development of a translational informatics framework to enable the discovery of drug-supplement interactions (DSIs) by linking scientific evidence from the biomedical literature. To response to NOT-AG-20-008, this administrative supplement application will complement our parent award in multiple aspects: (1) developing novel and advanced data analytic methods for mining RWD in EHR, (2) identifying DSs usage information among patients with ADRD, and (3) detecting safety and effect of DS among patients with ADRD from existing EHR data. In our preliminary work, we have investigated the methods to identify DSs terms on EHR and developed natural language processing (NLP) methods to identify use status of DSs. We will further our efforts to collect a EHR dataset with DSs usage and AE-DSs signals from AD patients and develop innovative informatics methods to extract such information. Our specific aims are: (1) identifying DSs usage among patients with MCI and ADRD from existing EHR data; and (2) detecting the DSs safety signals and exploring the effect of DSs use on the conversion from MCI to ADRD from existing EHR data. The successful completion of this project will stimulate our further investigation on the role of DS use in patients with ADRD in a larger scale involving EHR data from other healthcare institutions.

项目总结 大多数美国成年人(68%)服用膳食补充剂，而且越来越多的证据表明服用药物补充剂 互动(DSI)；近年来，越来越多的证据支持DSS在#年ADRD中的作用 预防认知障碍，但证据有限，样本量也很小。真正的- 世界数据(RWD)，特别是电子病历，包含患者的详细治疗和反应信息 可以用来检测DSS、DSI的使用和效果，这对临床结果更具转换性 (例如，MCI到ADRD的转换)。据我们所知，没有关于DSS使用情况的调查和 使用EHR数据在MCI和ADRD患者中的安全性。我们目前的家长奖主要集中在 开发翻译信息学框架，以发现药物与补充剂之间的相互作用 (DIS)通过将生物医学文献中的科学证据联系起来。为了回应NOT-AG-20-008，这 行政补充申请将在多个方面补充我们的家长奖励：(1)发展 用于EHR中RWD挖掘的新颖和先进的数据分析方法，(2)识别DSS使用信息 在ADRD患者中，以及(3)从现有的ADRD患者中检测DS的安全性和有效性 电子病历数据。在我们的初步工作中，我们研究了在电子病历上识别DSS术语的方法和 开发了自然语言处理(NLP)方法来识别决策支持系统的使用状态。我们会继续努力。 从AD患者收集包含DSS使用情况和AE-DSS信号的EHR数据集，并开发创新 提取此类信息的信息学方法。我们的具体目标是：(1)确定决策支持系统的使用情况 从现有的EHR数据中检测MCI和ADRD患者；以及(2)检测DSS安全信号并探索 DSS的使用对现有EHR数据从MCI到ADRD转换的影响。圆满完成 该项目的实施将促使我们进一步研究DS在ADRD患者中的作用。 涉及来自其他医疗机构的EHR数据的规模。