EMR Adverse Drug Event Detection for Pharmacovigilance

用于药物警戒的 EMR 药物不良事件检测

基本信息

批准号：
9123554
负责人：
HONG YU
金额：
$ 33.7万
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：
2014-09-01 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9123554
关键词：
Adverse drug event Adverse event Algorithms Antineoplastic Agents Biology Boxing Cancer Center Cancer Research Network Cessation of life Clinical Clinical Oncology Clinical Trials Collaborations Common Terminology Criteria for Adverse Events Comprehensive Cancer Center Computerized Medical Record Data Detection Discipline of Nursing Disease Drug toxicity Elements Future Goals Health Promotion Hematology Hospitals Informatics Injury Inpatients Intervention Knowledge Language Lead Left Machine Learning Malignant Neoplasms Manuals Maps Marketing Massachusetts Medical Methods Monitor Morbidity - disease rate Names Natural Language Processing Outpatients Patients Pattern Pharmaceutical Preparations Pharmacoepidemiology Prevention Public Health Records Reporting Research Research Personnel Resources Risk Safety Severities Signal Transduction Structure System Terminology Testing Therapeutic Toxic effect United States United States Food and Drug Administration Universities Weight Work abstracting disorder prevention firewall improved innovation insight lenalidomide medical schools mortality novel oncology open source patient safety post-market public health relevance response tool

项目摘要

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 In the United States, 20% of the 548 drugs introduced into the market between 1975 and 1999 were either withdrawn or acquired a new "black box" warning during the 25-year period following initial approval by the Food and Drug Administration.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

描述（由申请人提供）：不良药物事件（ADE）导致大量患者发病率，并每年导致100,000多人死亡。及时确定以前未知的癌症药物毒性是一个重要的，未解决的问题。在美国，在美国食品药品监督管理局初次批准后的25年中，在美国，在1975年至1999年间引入市场的548种药物中有20％被撤回或收到了新的“黑匣子”警告。不良药物事件是患者发病率和死亡率的重要原因，但有95％的ADE尚未报告，从而导致检测到以前未知的ADE的延迟，并低估了已知ADE的风险。众所周知，电子医疗记录（EMR），放电摘要和实验室结果包含ADE信息和生物医学自然语言处理（BIONLP）提供了自动化工具，可促进图表审查，从而改善患者的监视和市场后的药物治疗。该提案的目标是开发“智能” Bionlp方法来从EMR中提取疾病，药物和结构化信息，然后评估提取的ADE，以检测已知的ADE类型以及临床上未知或新颖的ADE或新型ADE，其模式或效果先前尚未被鉴定出来。