Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

Journals

1. Munkhdalai T, Liu F, Yu H. Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning. JMIR Public Health and Surveillance 2018;4(2):e29 View
2. Nikfarjam A, Sarker A, O’Connor K, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association 2015;22(3):671 View
3. Routray R, Tetarenko N, Abu-Assal C, Mockute R, Assuncao B, Chen H, Bao S, Danysz K, Desai S, Cicirello S, Willis V, Alford S, Krishnamurthy V, Mingle E. Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination. Drug Safety 2020;43(1):57 View
4. Alvaro N, Miyao Y, Collier N. TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations. JMIR Public Health and Surveillance 2017;3(2):e24 View
5. Beeksma M, Verberne S, van den Bosch A, Das E, Hendrickx I, Groenewoud S. Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Medical Informatics and Decision Making 2019;19(1) View
6. Terrier J, Daali Y, Fontana P, Csajka C, Reny J. Towards Personalized Antithrombotic Treatments: Focus on P2Y12 Inhibitors and Direct Oral Anticoagulants. Clinical Pharmacokinetics 2019;58(12):1517 View
7. Li F, Liu W, Yu H. 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 Medical Informatics 2018;6(4):e12159 View
8. Abatemarco D, Perera S, Bao S, Desai S, Assuncao B, Tetarenko N, Danysz K, Mockute R, Widdowson M, Fornarotto N, Beauchamp S, Cicirello S, Mingle E. Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing. Pharmaceutical Medicine 2018;32(6):391 View
9. Liu F, Jagannatha A, Yu H. Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records. Drug Safety 2019;42(1):95 View
10. Kaas‐Hansen B, Placido D, Rodríguez C, Thorsen‐Meyer H, Gentile S, Nielsen A, Brunak S, Jürgens G, Andersen S. Language‐agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records. Basic & Clinical Pharmacology & Toxicology 2022;131(4):282 View
11. Gaspar F, Lutters M, Beeler P, Lang P, Burnand B, Rinaldi F, Lovis C, Csajka C, Le Pogam M. Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal. JMIR Research Protocols 2022;11(11):e40456 View
12. T S, T C, MARPAKA S, K S. ASPECTS OF UTILIZATION AND LIMITATIONS OF ARTIFICIAL INTELLIGENCE IN DRUG SAFETY. Asian Journal of Pharmaceutical and Clinical Research 2021:34 View
13. Roosan D, Law A, Roosan M, Li Y. Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms. Journal of Medical Toxicology 2022;18(4):311 View
14. Zitu M, Zhang S, Owen D, Chiang C, Li L. Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records. Frontiers in Pharmacology 2023;14 View

Books/Policy Documents

1. Wunnava S, Qin X, Kakar T, Tlachac M, Kong X, Rundensteiner E, Sahoo S, De S. Biomedical Engineering Systems and Technologies. View
2. Guan M. Machine Learning and Deep Learning in Computational Toxicology. View