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EHR Anticoagulants Pharmacovigilance

EHR 抗凝剂药物警戒

基本信息

批准号：
9190384
负责人：
HONG YU
金额：
$ 117.74万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-12-01 至 2019-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9190384
关键词：
Adult Adverse drug event Adverse event Algorithms Anticoagulant therapy Anticoagulants Anticoagulation Area Atrial Fibrillation Biometry Cardiology Cardiovascular Agents Cardiovascular Diseases Classification Clinical Controlled Vocabulary Data Data Sources Detection Development Disease Electronic Health Record Elements Etiology Frequencies Health Care Costs Health Promotion Healthcare Hemorrhage Hospitals Illinois Injury Institutes Intervention Lead Length of Stay Linguistics Literature Machine Learning Manuals Maps Marketing Massachusetts Medical Methods Monitor Morbidity - disease rate Names Natural Language Processing Ontology Outcomes Research Package Insert Patients Pattern Pharmaceutical Preparations Pharmacotherapy Prevention Process Regimen Risk Risk Factors Route Safety Schedule Severities Signal Transduction Specific qualifier value Structure Supervision System Systems Analysis Text Thromboembolism Time Toxic effect United States United States Food and Drug Administration Universities Venous Work clinical practice cost data resource disorder prevention dosage epidemiology study high risk improved innovation mortality natural language novel open source patient safety predictive modeling prospective public health relevance tool

项目摘要

DESCRIPTION (provided by applicant): The timely identification of previously unknown toxicities of cardiovascular drugs is an important, unsolved problem. In the United States, 20% of the 548 drugs introduced into the market between 1975 and 1999 were either withdrawn or acquired a new "black box" warning during the 25-year period following initial approval by the Food and Drug Administration. Adverse drug events (ADEs) are an important cause of morbidity and mortality in patients, yet 95% of ADEs are unreported, leading to delays in the detection of previously unknown ADEs and underestimation of the risk to known ADEs. It is known that Electronic Health Record (EHR) notes and lab results contain ADE information and biomedical natural language processing (NLP) provides automated tools that facilitate chart review and thus improve patient surveillance and post-marketing pharmacovigilance. Optimal use of anticoagulants requires accurate and timely detection of ADEs from EHRs. The objectives for this proposal are to develop "intelligent" NLP approaches to extract disease, medication, and structured ADE information from EHRs, and then evaluate extracted ADEs for detecting known ADE types as well as clinically unrecognized or novel ADEs whose pattern or effect have not been previously identified. PHS 398/2590 (Rev. 06/09) Page Continuation Format Page

描述(由申请人提供)：及时识别以前未知的心血管药物毒性是一个重要的、尚未解决的问题。在美国，在1975年至1999年期间推出市场的548种药物中，有20%在食品和药物管理局初步批准后的25年内被撤回或获得了新的“黑匣子”警告。药物不良事件(ADE)是导致患者发病率和死亡率的重要原因，但95%的ADE没有报告，导致对以前未知的ADE的检测延迟，并低估了已知ADE的风险。众所周知，电子健康记录(EHR)笔记和实验室结果包含ADE信息，而生物医学自然语言处理(NLP)提供了自动化工具，促进了图表审查，从而改善了患者监测和上市后的药物警戒。抗凝剂的最佳使用需要准确和及时地检测EHR中的ADE。这项建议的目标是开发“智能”的NLP方法，从EHR中提取疾病、药物和结构化的ADE信息，然后评估提取的ADE，以检测已知的ADE类型以及临床上未识别的或以前尚未确定其模式或效果的新型ADE。PHS 398/2590(06/09版)页面续格式页面