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 In the United States, 20% of the 548 drugs introduced into the market between 1975 and 1999 were either withdrawn or acquired a new black box warning during the 25-year period following initial approval by the Food and Drug Administration.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

Public Health Relevance

This project proposes innovative intelligent biomedical natural language process approaches to detect ADEs in patients' electronic health records (EHRs), an important step towards ADE surveillance and pharmacovigilance. The focus of this proposal will bon the national priority area of anticoagulants ADE detection, although the algorithms and systems we develop can be extended to other drug classes and diseases. It is anticipated that the data resources, algorithms, and the Anticoagulant Pharmacovigilance Toolkit (APVTK) developed will significantly enhance ADE detection, patient surveillance and post marketing pharmacovigilance.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL125089-03
Application #
9190384
Study Section
Special Emphasis Panel (ZRG1-BCHI-Q (04))
Program Officer
Chang, Henry
Project Start
2014-12-01
Project End
2019-11-30
Budget Start
2016-12-01
Budget End
2017-11-30
Support Year
3
Fiscal Year
2017
Total Cost
$1,177,432
Indirect Cost
$379,741
Name
University of Massachusetts Medical School Worcester
Department
Other Health Professions
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655
Tran, Hoang V; Ash, Arlene S; Gore, Joel M et al. (2018) Twenty-five year trends (1986-2011) in hospital incidence and case-fatality rates of ventricular tachycardia and ventricular fibrillation complicating acute myocardial infarction. Am Heart J 208:1-10
Pradhan, Richeek; Garnick, Kyle; Barkondaj, Bikramjit et al. (2018) Inadequate diversity of information resources searched in US-affiliated systematic reviews and meta-analyses: 2005-2016. J Clin Epidemiol 102:50-62
Tran, Hoang V; Lessard, Darleen; Tisminetzky, Mayra S et al. (2018) Trends in Length of Hospital Stay and the Impact on Prognosis of Early Discharge After a First Uncomplicated Acute Myocardial Infarction. Am J Cardiol 121:397-402
Tran, Hoang V; Gore, Joel M; Darling, Chad E et al. (2018) Hyperglycemia and risk of ventricular tachycardia among patients hospitalized with acute myocardial infarction. Cardiovasc Diabetol 17:136
Li, Fei; Liu, Weisong; Yu, Hong (2018) Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning. JMIR Med Inform 6:e12159
Alcusky, Matthew; Hume, Anne L; Fisher, Marc et al. (2018) Dabigatran Versus Rivaroxaban for Secondary Stroke Prevention in Patients with Atrial Fibrillation Rehabilitated in Skilled Nursing Facilities. Drugs Aging 35:1089-1098
Munkhdalai, Tsendsuren; Liu, Feifan; Yu, Hong (2018) Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning. JMIR Public Health Surveill 4:e29
Rumeng, Li; Abhyuday N, Jagannatha; Hong, Yu (2017) A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes. AMIA Annu Symp Proc 2017:1149-1158
Munkhdalai, Tsendsuren; Yu, Hong (2017) Neural Semantic Encoders. Proc Conf Assoc Comput Linguist Meet 1:397-407
Munkhdalai, Tsendsuren; Yu, Hong (2017) Neural Tree Indexers for Text Understanding. Proc Conf Assoc Comput Linguist Meet 1:11-21

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