This paper is in the following e-collection/theme issue:

Pharmacovigilance (129) Infodemiology and Infoveillance (1278) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015)

Published on in Vol 3, No 2 (2017): Apr-Jun

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

Authors of this article:

Nestor Alvaro1, 2 Author Orcid Image ;   Yusuke Miyao1, 2 Author Orcid Image ;   Nigel Collier3 Author Orcid Image
Article Authors Cited by (46) Tweetations (21) Metrics

Journals

  1. Dreisbach C, Koleck T, Bourne P, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International Journal of Medical Informatics 2019;125:37 View
  2. Guetterman T, Chang T, DeJonckheere M, Basu T, Scruggs E, Vydiswaran V. Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study. Journal of Medical Internet Research 2018;20(6):e231 View
  3. Zhang P, Wu H, Chiang C, Wang L, Binkheder S, Wang X, Zeng D, Quinney S, Li L. Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research. CPT: Pharmacometrics & Systems Pharmacology 2018;7(2):90 View
  4. Zhang T, Lin H, Ren Y, Yang L, Xu B, Yang Z, Wang J, Zhang Y. Adverse drug reaction detection via a multihop self-attention mechanism. BMC Bioinformatics 2019;20(1) View
  5. Correia R, Wood I, Bollen J, Rocha L. Mining Social Media Data for Biomedical Signals and Health-Related Behavior. Annual Review of Biomedical Data Science 2020;3(1):433 View
  6. Li Z, Yang Z, Luo L, Xiang Y, Lin H. Exploiting adversarial transfer learning for adverse drug reaction detection from texts. Journal of Biomedical Informatics 2020;106:103431 View
  7. Dietrich J, Gattepaille L, Grum B, Jiri L, Lerch M, Sartori D, Wisniewski A. Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR. Drug Safety 2020;43(5):467 View
  8. Lou J, Zhang Y. Semantic change analysis of Korean verbs based on massive culture corpus data. Personal and Ubiquitous Computing 2020;24(1):115 View
  9. Pappa D, Stergioulas L. Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. International Journal of Data Science and Analytics 2019;8(2):113 View
  10. Gattepaille L, Hedfors Vidlin S, Bergvall T, Pierce C, Ellenius J. Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project. Drug Safety 2020;43(8):797 View
  11. Pérez-Pérez M, Pérez-Rodríguez G, Fdez-Riverola F, Lourenço A. Collaborative relation annotation and quality analysis in Markyt environment. Database 2017;2017 View
  12. Alimova I, Tutubalina E. Entity-Level Classification of Adverse Drug Reaction: A Comparative Analysis of Neural Network Models. Programming and Computer Software 2019;45(8):439 View
  13. Walsh J, Cave J, Griffiths F. Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis. Frontiers in Digital Health 2021;3 View
  14. Tutubalina E, Alimova I, Miftahutdinov Z, Sakhovskiy A, Malykh V, Nikolenko S, Wren J. The Russian Drug Reaction Corpus and neural models for drug reactions and effectiveness detection in user reviews. Bioinformatics 2021;37(2):243 View
  15. Kang K, Tian S, Yu L. Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning. Journal of Intelligent & Fuzzy Systems 2021;40(5):8899 View
  16. Zhang T, Lin H, Ren Y, Yang Z, Wang J, Duan X, Xu B. Identifying adverse drug reaction entities from social media with adversarial transfer learning model. Neurocomputing 2021;453:254 View
  17. Hussain S, Afzal H, Saeed R, Iltaf N, Umair M, Sari M. Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM. Computational and Mathematical Methods in Medicine 2021;2021:1 View
  18. Scaboro S, Portelli B, Chersoni E, Santus E, Serra G. Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation. Experimental Biology and Medicine 2022;247(22):2003 View
  19. Weissenbacher D, O’Connor K, Rawal S, Zhang Y, Tsai R, Miller T, Xu D, Anderson C, Liu B, Han Q, Zhang J, Kulev I, Köprü B, Rodriguez-Esteban R, Ozkirimli E, Ayach A, Roller R, Piccolo S, Han P, Vydiswaran V, Tekumalla R, Banda J, Bagherzadeh P, Bergler S, Silva J, Almeida T, Martinez P, Rivera-Zavala R, Wang C, Dai H, Alberto Robles Hernandez L, Gonzalez-Hernandez G. Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition. Database 2023;2023 View
  20. Zhang T, Lin H, Xu B, Yang L, Wang J, Duan X. Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions. Journal of Biomedical Informatics 2021;123:103896 View
  21. Sboev A, Sboeva S, Moloshnikov I, Gryaznov A, Rybka R, Naumov A, Selivanov A, Rylkov G, Ilyin V. Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models. Applied Sciences 2022;12(1):491 View
  22. Tan H, Teo C, Ang P, Loke W, Tham M, Tan S, Soh B, Foo P, Ling Z, Yip W, Tang Y, Yang J, Tung K, Dorajoo S. Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries. Drug Safety 2022;45(8):853 View
  23. Sboev A, Rybka R, Selivanov A, Moloshnikov I, Gryaznov A, Naumov A, Sboeva S, Rylkov G, Zakirova S. Accuracy Analysis of the End-to-End Extraction of Related Named Entities from Russian Drug Review Texts by Modern Approaches Validated on English Biomedical Corpora. Mathematics 2023;11(2):354 View
  24. Wang X, Wang X, Zhang S. Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention. IEEE Access 2023;11:16194 View
  25. Tao D, Zhang D, Hu R, Rundensteiner E, Feng H. Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media. Scientific Reports 2021;11(1) View
  26. Mredula M, Dey N, Rahman M, Mahmud I, Cho Y. A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data. Sensors 2022;22(12):4531 View
  27. 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
  28. Karapetian K, Jeon S, Kwon J, Suh Y. Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus. Journal of Medical Internet Research 2023;25:e41100 View
  29. Wu H, Wang M, Wu J, Francis F, Chang Y, Shavick A, Dong H, Poon M, Fitzpatrick N, Levine A, Slater L, Handy A, Karwath A, Gkoutos G, Chelala C, Shah A, Stewart R, Collier N, Alex B, Whiteley W, Sudlow C, Roberts A, Dobson R. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. npj Digital Medicine 2022;5(1) View
  30. Zhang Y, Lee J, Han J, Tsai R. Task reformulation and data-centric approach for Twitter medication name extraction. Database 2022;2022 View
  31. He K, Mao R, Gong T, Cambria E, Li C. JCBIE: a joint continual learning neural network for biomedical information extraction. BMC Bioinformatics 2022;23(1) View
  32. Trajanov D, Trajkovski V, Dimitrieva M, Dobreva J, Jovanovik M, Klemen M, Žagar A, Robnik-Šikonja M, Khoshbouei H. Review of Natural Language Processing in Pharmacology. Pharmacological Reviews 2023;75(4):714 View
  33. Sakhovskiy A, Tutubalina E. Cross-Lingual Transfer Learning in Drug-Related Information Extraction from User-Generated Texts. Programming and Computer Software 2023;49(7):590 View
  34. Schmidt L, Mohamed S, Meader N, Bacardit J, Craig D. Automated data analysis of unstructured grey literature in health research: A mapping review. Research Synthesis Methods 2024;15(2):178 View
  35. Remy F, Demuynck K, Demeester T. BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights. Journal of the American Medical Informatics Association 2024 View
  36. Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine 2024;154:102900 View

Books/Policy Documents

  1. Alimova I, Solovyev V. Artificial Intelligence and Natural Language. View
  2. Arguello-Casteleiro M, Jones P, Robertson S, Irvine R, Twomey F, Nenadic G. Artificial Intelligence XXXVI. View
  3. Gupta S, Gupta M, Varma V, Pawar S, Ramrakhiyani N, Palshikar G. Advances in Information Retrieval. View
  4. Gupta S, Gupta M, Varma V, Pawar S, Ramrakhiyani N, Palshikar G. Advances in Information Retrieval. View
  5. Wang X, Huang W, Zhang S. Intelligent Computing Theories and Application. View
  6. Gao Y, Ji S, Zhang T, Tiwari P, Marttinen P. Machine Learning and Knowledge Discovery in Databases. View
  7. Qiu Y, Zhang X, Wang W, Zhang T, Xu B, Lin H. Natural Language Processing and Chinese Computing. View
  8. Xu H, Demner Fushman D, Hong N, Raja K. Natural Language Processing in Biomedicine. View