Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Journals

1. Berthelot J, Darrieutort-Laffite C, Le Goff B. Usefulness of real-world patient cohort follow-ups using questionnaires to assess the effect of treatments on the general population. Joint Bone Spine 2021;88(4):105142 View
2. Berthelot J, Darrieutort-Laffite C, Le Goff B. Utilité des suivis de cohortes de patients de la vraie-vie par questionnaires pour évaluer l’effet des traitements sur l’ensemble de la population. Revue du Rhumatisme 2022;89(2):107 View
3. Shin H, Cha J, Lee C, Song H, Jeong H, Kim J, Lee S. The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance. Applied Sciences 2021;11(5):2249 View
4. Ibrahim H, Abdo A, El Kerdawy A, Eldin A. Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources. Artificial Intelligence in the Life Sciences 2021;1:100005 View
5. Narayanan S, Mannam K, Achan P, Ramesh M, Rangan P, Rajan S. A contextual multi-task neural approach to medication and adverse events identification from clinical text. Journal of Biomedical Informatics 2022;125:103960 View
6. Teramoto K, Takeda T, Mihara N, Shimai Y, Manabe S, Kuwata S, Kondoh H, Matsumura Y. Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study. JMIR Medical Informatics 2021;9(11):e28763 View
7. de Oliveira J, da Costa C, Antunes R. Data structuring of electronic health records: a systematic review. Health and Technology 2021;11(6):1219 View
8. Murphy R, Klopotowska J, de Keizer N, Jager K, Leopold J, Dongelmans D, Abu-Hanna A, Schut M, Qamar U. Adverse drug event detection using natural language processing: A scoping review of supervised learning methods. PLOS ONE 2023;18(1):e0279842 View
9. Ramachandran G, Lybarger K, Liu Y, Mahajan D, Liang J, Tsou C, Yetisgen M, Uzuner Ö. Extracting medication changes in clinical narratives using pre-trained language models. Journal of Biomedical Informatics 2023;139:104302 View
10. Wang Q, Li C. Evaluating risk propagation in renewable energy incidents using ontology-based Bayesian networks extracted from news reports. International Journal of Green Energy 2022;19(12):1290 View
11. Timilsina M, Tandan M, Nováček V. Machine learning approaches for predicting the onset time of the adverse drug events in oncology. Machine Learning with Applications 2022;9:100367 View
12. Breit A, Waltersdorfer L, Ekaputra F, Sabou M, Ekelhart A, Iana A, Paulheim H, Portisch J, Revenko A, Teije A, Van Harmelen F. Combining Machine Learning and Semantic Web: A Systematic Mapping Study. ACM Computing Surveys 2023;55(14s):1 View
13. Timilsina M, Tandan M, Nováček V. Machine Learning Approaches for Predicting the Onset Time of the Adverse Drug Events in Oncology. SSRN Electronic Journal 2022 View
14. Botsis T, Kreimeyer K. Improving drug safety with adverse event detection using natural language processing. Expert Opinion on Drug Safety 2023;22(8):659 View
15. Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. Journal of Biomedical Informatics 2023;143:104418 View
16. Herman Bernardim Andrade G, Nishiyama T, Fujimaki T, Yada S, Wakamiya S, Takagi M, Kato M, Miyashiro I, Aramaki E. Assessing Domain Adaptation in Adverse Drug Event Extraction on Real-World Breast Cancer Records. International Journal of Medical Informatics 2024:105539 View

Books/Policy Documents

1. Waltersdorfer L, Breit A, Ekaputra F, Sabou M. Database and Expert Systems Applications - DEXA 2021 Workshops. View
2. Devarakonda M, Raja K, Xu H. Natural Language Processing in Biomedicine. View