EMR Adverse Drug Event Detection for Pharmacovigilance

描述（由申请人提供）：药物不良事件（ADE）导致大量患者发病，每年导致超过100，000例死亡。及时识别癌症药物的先前未知的毒性是一个重要的，未解决的问题。在美国，1975年至1999年期间引入市场的548种药物中有20%在食品和药物管理局首次批准后的25年期间被撤回或获得新的“黑匣子”警告。药物不良事件是导致患者发病和死亡的重要原因，但95%的ADE未报告，导致先前未知ADE的检测延迟和已知ADE风险的低估。众所周知，电子病历（EMR）、出院总结和实验室结果包含ADE信息，生物医学自然语言处理（BioNLP）提供了自动化工具，有助于病历审查，从而改善患者监测和上市后药物警戒。该提案的目的是开发“智能”BioNLP方法，从EMR中提取疾病、药物和结构化ADE信息，然后评价提取的ADE，以检测已知ADE类型以及临床上未识别或新型ADE（其模式或效应先前尚未确定）。

DESCRIPTION (provided by applicant): Adverse drug events (ADEs) result in substantial patient morbidity and lead to over 100,000 deaths yearly. The timely identification of previously unknown toxicities of cancer 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 In the United States, 20% of the 548 drugs introduced into the market between 1975 and 1999 were either withdrawn or acquired a new "black box" warning during the 25-year period following initial approval by the Food and Drug Administration.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 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 Medical Record (EMR), discharge summaries, and lab results contain ADE information and biomedical natural language processing (BioNLP) provides automated tools that facilitate chart review and thus improve patient surveillance and post-marketing pharmacovigilance. The objectives for this proposal are to develop "intelligent" BioNLP approaches to extract disease, medication, and structured ADE information from EMRs, 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.