EHR Anticoagulants Pharmacovigilance

描述（由申请人提供）：及时识别先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，1975年至1999年间上市的548种药物中，有20%要么被撤回，要么在食品和药物管理局最初批准后的25年时间里获得了新的“黑盒子”警告。药物不良事件（Adverse drug events, ADEs）是导致患者发病和死亡的重要原因，但95%的ade未被报道，这导致了对以前未知的ade的延迟检测和对已知ade风险的低估。众所周知，电子健康记录（EHR）笔记和实验室结果包含ADE信息，生物医学自然语言处理（NLP）提供自动化工具，促进图表审查，从而改善患者监测和上市后药物警戒。最佳使用抗凝血剂需要从电子病历中准确及时地检测ade。本提案的目标是开发“智能”NLP方法，从电子病历中提取疾病、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，以及临床未识别的或以前未确定模式或效果的新型ADE。小灵通398/2590 （Rev. 06/09）页延续格式页

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 In the United States, 20% of the 548 drugs introduced into the market between 1975 and 1999 were either withdrawn or acquired a new "black box" warning during the 25-year period following initial approval by the Food and Drug Administration.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