Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting
Open accessAbstract
Objective: To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. Materials and Methods: We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers. Results: Our system was among the top ranked. Our best model submitted during this challenge (based on recurrent neural networks and support vector machines) achieved lenient F1 scores of 0.9287 for concept extraction (ranked third), 0.9459 for relation classification (ranked fourth), and 0.8778 for the end-to-end relation extraction (ranked second). We developed a novel named entity recognition model based on a recurrent convolutional neural network and further investigated gradient boosting for relation classification. The new methods improved the lenient F1 scores of the 3 subtasks to 0.9292, 0.9633, and 0.8880, respectively, which are comparable to the best performance reported in this challenge. Conclusion: This study demonstrated the feasibility of using machine learning methods to extract the relations of medications with adverse drug events from clinical narratives.
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Previous studies developed individual classifiers to handle relations with different cross-distance.
“Previous studies [44,45] developed individual classifiers to handle relations with different cross-distance”
From Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models (2020) · cites this paper as [45]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Institute of Medicine (2000) · National Academies Press eBooks · doi:10.17226/9728 · Cited by 14,174 · see citing works
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,882 · see citing works
The unified medical language system (UMLS): integrating biomedical terminology
Bodenreider, O. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh061 · Cited by 4,356 · see citing works
Natural language processing: an introduction
Nadkarni, P. M., Lucila Ohno-machado, A., & Chapman, W. W. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000464 · Cited by 1,984 · see citing works
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967 · see citing works
Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., & Altün, Y. (2005) · MPG.PuRe (Max Planck Society) · Cited by 1,879 · see citing works
An overview of MetaMap: historical perspective and recent advances
Aronson, A. R., & Lang, F.-M. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.002733 · Cited by 1,519 · see citing works
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Uzuner, Ö., South, B. R., Shen, S., & DuVall, S. L. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000203 · Cited by 1,258 · see citing works
Clinical information extraction applications: A literature review
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.011 · Cited by 910 · see citing works
A side effect resource to capture phenotypic effects of drugs
Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.98 · Cited by 899 · see citing works
Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., & Meystre, S. M. (2008) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638592 · Cited by 895 · see citing works
A General Natural-language Text Processor for Clinical Radiology
Friedman, C., Alderson, P. O., Austin, J. H. M., Cimino, J. J., & Johnson, S. B. (1994) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1994.95236146 · Cited by 775 · see citing works
Proceedings of the 26th Annual International Conference on Machine Learning
Danyluk, A., Bottou, L., & Littman, M. L. (2009) · Cited by 686 · see citing works
Proceedings of the Eighteenth International Conference on Machine Learning
Brodley, C. E., & Danyluk, A. (2001) · Cited by 566 · see citing works
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
Sun, W., Rumshisky, A., & Uzuner, Ö. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001628 · Cited by 477 · see citing works
Bidirectional RNN for Medical Event Detection in Electronic Health Records
Jagannatha, A. N., & Yu, H. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1056 · Cited by 314 · see citing works
Jiang, M., Chen, Y., Liu, M., Rosenbloom, S. T., Mani, S., Denny, J. C., & Xu, H. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000163 · Cited by 292 · see citing works
Overview of the ShARe/CLEF eHealth Evaluation Lab 2013
Suominen, H., Salanterä, S., Velupillai, S. U., Chapman, W. W., Savova, G., Elhadad, N., Pradhan, S., South, B. R., Mowery, D. L., Jones, G. J. F., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40802-1_24 · Cited by 255 · see citing works
de Bruijn, B., Cherry, C. A., Kiritchenko, S., Martin, J., & Zhu, X. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000150 · Cited by 235 · see citing works
Entity recognition from clinical texts via recurrent neural network
Liu, Z., Yang, M. Y., Wang, X., Chen, Q., Tang, B., Wang, Z., & Xu, H. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0468-7 · Cited by 176 · see citing works
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network
Wu, Y., Min, J., Jianbo, L., & Xu, H. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-624 · Cited by 148 · see citing works
Self-citationFriedman, C., Rindflesch, T. C., & Corn, M. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.06.004 · Cited by 127 · see citing works
Tang, B., Cao, H., Wu, Y., Jiang, M., & Xu, H. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-s1-s1 · Cited by 126 · see citing works
Self-citationA hybrid system for temporal information extraction from clinical text
Tang, B., Wu, Y., Jiang, M., Chen, Y., Denny, J. C., & Xu, H. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001635 · Cited by 117 · see citing works
Self-citationClinical Named Entity Recognition Using Deep Learning Models.
Wu, Y., Jiang, M., Xu, J., Zhi, D., & Xu, H. (2017) · PubMed Central · Cited by 109 · see citing works
Self-citationStausberg, J. (2014) · BMC Health Services Research · doi:10.1186/1472-6963-14-125 · Cited by 99 · see citing works
A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.
Wu, Y., Xu, J., Jiang, M., Zhang, Y., & Xu, H. (2015) · PubMed Central · Cited by 93 · see citing works
Self-citationPoudel, D. R., Acharya, P., Ghimire, S., Dhital, R., & Bharati, R. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4184 · Cited by 86 · see citing works
The KnowledgeMap project: development of a concept-based medical school curriculum database.
Denny, J. C., Irani, P. R., Wehbe, F., Smithers, J. D., & Spickard, A. (2003) · Europe PMC (PubMed Central) · Cited by 84 · see citing works
CD-REST: a system for extracting chemical-induced disease relation in literature
Xu, J., Wu, Y., Zhang, Y., Wang, J., Lee, H.-J., & Xu, H. (2016) · Database · doi:10.1093/database/baw036 · Cited by 79 · see citing works
Self-citationRecurrent convolutional neural network for answer selection in community question answering
Zhou, X., Hu, B., Chen, Q., & Wang, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.07.082 · Cited by 78 · see citing works
Liu, F., Jagannatha, A., & Yu, H. Q. (2019) · Drug Safety · doi:10.1007/s40264-018-0766-8 · Cited by 74 · see citing works
Yang, X., Bian, J., Gong, Y., Hogan, W. R., & Wu, Y. (2019) · Drug Safety · doi:10.1007/s40264-018-0761-0 · Cited by 66 · see citing works
Self-citationWunnava, S., Qin, X., Kakar, T., Şen, C., Rundensteiner, E. A., & Kong, X. (2019) · Drug Safety · doi:10.1007/s40264-018-0765-9 · Cited by 63 · see citing works
Akkasi, A., Varoğlu, E., & Dimililer, N. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0920-5 · Cited by 42 · see citing works
Wu, Y., Yang, X., Bian, J., Guo, Y. M., Xu, H., & Hogan, W. R. (2018) · Europe PMC (PubMed Central) · Cited by 29 · see citing works
Self-citationInformation Access Evaluation. Multilinguality, Multimodality, and Visual Analytics
Catarci, T., Santucci, G., Peñas, A., Hiemstra, D., & Forner, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33247-0 · Cited by 25 · see citing works
Flagged first, then by citations
2 of the 46 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847 · see citing works
Self-citationClinical concept extraction using transformers
Yang, X., Bian, J., Hogan, W. R., & Wu, Y. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa189 · Cited by 155 · see citing works
Self-citationMachine learning approaches for electronic health records phenotyping: a methodical review
Yang, S., Varghese, P., Stephenson, E., Tu, K., & Gronsbell, J. L. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac216 · Cited by 142 · see citing works
Murphy, R. M., Klopotowska, J. E., de Keizer, N. F., Jager, K. J., Leopold, J. H., Dongelmans, D. A., Abu–Hanna, A., & Schut, M. C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279842 · Cited by 98 · see citing works
Fraile Navarro, D., Ijaz, K., Rezazadegan, D., Rahimi-Ardabili, H., Dras, M., Coiera, E., & Berkovsky, S. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105122 · Cited by 82 · see citing works
Li, Y., Tao, W., Li, Z., Sun, Z., Fang, L., Fenton, S. H., Xu, H., & Tao, C. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104621 · Cited by 77 · see citing works
Cheng, P., Yang, X., Smith, K. E., Yu, Z., Chen, A., Bian, J., & Wu, Y. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104630 · Cited by 73 · see citing works
Self-citationXAI-Based Clinical Decision Support Systems: A Systematic Review
Kim, S. Y., Kim, D. H., Kim, M., Ko, H.-J., & Jeong, O. (2024) · Applied Sciences · doi:10.3390/app14156638 · Cited by 84 · see citing works
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69 · see citing works
Named Entity Recognition in Electronic Health Records: A Methodological Review
Durango, M. C., Torres-Silva, E. A., & Orozco‐Duque, A. (2023) · Healthcare Informatics Research · doi:10.4258/hir.2023.29.4.286 · Cited by 54 · see citing works
Artificial intelligence, machine learning, and deep learning in women’s health nursing
Jeong, G. H. (2020) · Korean Journal of Women Health Nursing · doi:10.4069/kjwhn.2020.03.11 · Cited by 51 · see citing works
MTTLADE: A multi-task transfer learning-based method for adverse drug events extraction
El-allaly, E.-D., Sarrouti, M., En-Nahnahi, N., & Alaoui, S. O. E. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2020.102473 · Cited by 47 · see citing works
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
Modi, S., Kasmiran, K. A., Sharef, N. M., & Sharum, M. Y. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104603 · Cited by 44 · see citing works
Contextualized medication information extraction using Transformer-based deep learning architectures
Chen, A., Yu, Z., Yang, X., Guo, Y., Bian, J., & Wu, Y. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104370 · Cited by 40 · see citing works
Self-citationJouffroy, J., Feldman, S. F., Lerner, I., Rance, B., Burgun, A., & Neuraz, A. (2021) · JMIR Medical Informatics · doi:10.2196/17934 · Cited by 40 · see citing works
Deimazar, G., & Sheikhtaheri, A. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105246 · Cited by 45 · see citing works
Enhancing Postmarketing Surveillance of Medical Products With Large Language Models
Matheny, M. E., Yang, J., Smith, J., Walsh, C. G., Al-Garadi, M. A., Davis, S. E., Marsolo, K. A., Fabbri, D., Reeves, R. R., Johnson, K. B., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.28276 · Cited by 41 · see citing works
Yu, Z., Peng, C., Yang, X., Dang, C., Adekkanattu, P., Gopal Patra, B., Peng, Y., Pathak, J., Wilson, D. L., Chang, C.-Y., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104642 · Cited by 37 · see citing works
Self-citationCheng, P., Yang, X., Chen, A., Yu, Z., Smith, K. E., Costa, A. B., Flores, M. G., Bian, J., & Wu, Y. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae078 · Cited by 30 · see citing works
Self-citationGatorTron: A Large Language Model for Clinical Natural Language Processing
Yang, X., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., et al. (2022) · doi:10.1101/2022.02.27.22271257 · Cited by 33 · see citing works
Self-citationEl-allaly, E.-D., Sarrouti, M., En-Nahnahi, N., & Alaoui, S. O. E. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103968 · Cited by 32 · see citing works
Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study
Alfattni, G., Belousov, M., Peek, N. B., & Nenadić, G. (2021) · JMIR Medical Informatics · doi:10.2196/24678 · Cited by 31 · see citing works
Dandala, B., Joopudi, V., Tsou, C.-H., Liang, J. J., & Suryanarayanan, P. (2020) · JMIR Medical Informatics · doi:10.2196/18417 · Cited by 29 · see citing works
Yang, X., Zhang, H., He, X., Bian, J., & Wu, Y. (2020) · JMIR Medical Informatics · doi:10.2196/22982 · Cited by 29 · see citing works
Self-citationClinical concept and relation extraction using prompt-based machine reading comprehension
Cheng, P., Yang, X., Yu, Z., Bian, J., Hogan, W. R., & Wu, Y. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad107 · Cited by 32 · see citing works
Self-citationMajdik, Z. P., Graham, S. S., Edward, J. C. S., Rodriguez, S. N., Karnes, M. S., Jensen, J. T., Barbour, J. B., & Rousseau, J. F. (2024) · JMIR AI · doi:10.2196/52095 · Cited by 29 · see citing works
Silverman, G., Sahoo, H. S., Ingraham, N. E., Lupei, M. I., Puskarich, M. A., Usher, M. G., Dries, J., Finzel, R., Murray, E., Sartori, J., et al. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12631 · Cited by 27 · see citing works
Extracting Adverse Drug Events from Clinical Notes
Mahendran, D., & McInnes, B. T. (2021) · PubMed Central · doi:10.48550/arxiv.2104.10791 · Cited by 27 · see citing works
Improving drug safety with adverse event detection using natural language processing
Botsis, T., & Kreimeyer, K. (2023) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2023.2228197 · Cited by 24 · see citing works
Yu, Z., Yang, X., Guo, Y. M., Bian, J., & Wu, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.778463 · Cited by 22 · see citing works
Self-citationXia, L. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113832 · Cited by 23 · see citing works
Alamoodi, A. H., Zughoul, O., David, D., Garfan, S., Pamucar, D., Albahri, O. S., Albahri, A. S., Yussof, S., & Sharaf, I. M. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02090-y · Cited by 22 · see citing works
A novel discrete learning-based intelligent methodology for breast cancer classification purposes
Khashei, M., & Bakhtiarvand, N. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102492 · Cited by 14 · see citing works
Advancing the state of the art in automatic extraction of adverse drug events from narratives
Uzuner, Ö., Stubbs, A., & Lenert, L. A. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz206 · Cited by 14 · see citing works
Predicting Stroke and Mortality in Mitral Regurgitation: A Machine Learning Approach
Zhou, J., Lee, S., Liu, Y., Chan, J. S. K., Li, G., Wong, W. T., Jeevaratnam, K., Cheng, S. H., Liu, T., Tse, G., et al. (2022) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2022.101464 · Cited by 13 · see citing works
MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction
Dai, X., Karimi, S., Sarker, A., Hachey, B., & Paris, C. L. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104744 · Cited by 14 · see citing works
Das, M., Deb, C. K., Pal, R., & Marwaha, S. (2022) · Applied Sciences · doi:10.3390/app12094770 · Cited by 11 · see citing works
Clinical Relation Extraction Using Transformer-based ModelsPreprint
Yang, X., Yu, Z., Guo, Y., Bian, J., & Wu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08957 · Cited by 23 · see citing works
Self-citationGenerative Artificial Intelligence-Based Medical Entity Data Extractor Using Large Language ModelsPreprint
Ghali, M.-K., Farrag, A., Sakai, H., baz, H. E., Jin, Y., & Lam, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5063216 · Cited by 11 · see citing works
Yang, S. C., Yang, X., Lyu, T., Huang, J. G., Chen, A., He, X., Braithwaite, D., Mehta, H. J., Wu, Y., Guo, Y. M., et al. (2024) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-024-00166-5 · Cited by 10 · see citing works
Self-citationExtraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach
Silva, J., Almeida, J. R., & Matos, S. (2020) · JMIR Medical Informatics · doi:10.2196/22898 · Cited by 10 · see citing works
The future of artificial intelligence in nursing
Özsezer, G. (2022) · Journal of Human Sciences · doi:10.14687/jhs.v19i2.6217 · Cited by 9 · see citing works
Colicchio, T. K., & Cimino, J. J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104508 · Cited by 8 · see citing works
Adverse Drug Reaction extraction: Tolerance to entity recognition errors and sub-domain variants
Santiso, S., Pérez, A., & Casillas, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105891 · Cited by 9 · see citing works
Entity relation extraction in the medical domain: based on data augmentation
Wang, A., Li, L., Wu, X., Jianping, Z., Yu, S., Chen, X., Li, J., & Zhu, H. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-3991 · Cited by 8 · see citing works
Evaluation of a Prescription Outlier Detection System in Hospital’s Pharmacy Services
dos Santos, H. D. P., Ulbrich, A. H. D. P. S., & Vieira, R. L. R. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669703 · Cited by 6 · see citing works
Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels
Gisladottir, U., Zietz, M., Kivelson, S., Tanaka, Y., Sirdeshmukh, G., Brown, K. L., & Tatonetti, N. P. (2025) · Drug Safety · doi:10.1007/s40264-025-01594-x · Cited by 6 · see citing works
Artificial neural network (ANN) in drug delivery
Piroozmand, F., Mohammadipanah, F., & Sajedi, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-89925-3.00005-8 · Cited by 6 · see citing works
Lu, X., Tong, J., & Xia, S. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024058 · Cited by 5 · see citing works
Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials
Jimenez‐Maggiora, G. A., Donohue, M., Rafii, M. S., Raman, R., & Aisen, P. S. S. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2024.100002 · Cited by 4 · see citing works
Kopacheva, E., Henriksson, A., Dalianis, H., Hammar, T., & Lincke, A. (2025) · JMIR Formative Research · doi:10.2196/71949 · Cited by 4 · see citing works
Adverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users
Sboev, A. G., Борисович, Р. Р., Gryaznov, A. V., Александрович, М. И., Sboeva, S., Rylkov, G., & Selivanov, A. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040145 · Cited by 4 · see citing works
El-allaly, E.-D., Oubelkacem, A., & Bourray, H. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00214-8 · Cited by 2 · see citing works
AI-Based Automation for Medication Reconciliation: Scoping Review
Bortesi, J. P. T., Becerra, M. P., Ranisau, J., Wen, B., Nadesan, P., Devereaux, P. J., McGillion, M. H., & Petch, J. (2026) · Journal of Medical Internet Research · doi:10.2196/86760 · Cited by 2 · see citing works
Schreier, O., Yazdani, A., Galdadas, I., Kabak, R., Gervasio, F. L., Mu, G., & Teodoro, D. (2026) · JMIR AI · doi:10.2196/77732 · Cited by 2 · see citing works
Cyclic Convolutional Neural Network Model Based on Artificial Intelligence
Ye, T., Wang, G., & Cai, C. (2023) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2023.1.00300 · Cited by 2 · see citing works
Prompting for Few-shot Adverse Drug Reaction Recognition from Online Reviews
Chang, C., Chang, F., Hwang, S., & Yang, C. C. (2023) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi57859.2023.00032 · Cited by 1 · see citing works
Yang, S., Yang, X., Lyu, T., He, X., Braithwaite, D., Mehta, H. J., Guo, Y. M., Wu, Y., & Bian, J. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00125 · Cited by 2 · see citing works
Self-citationNatural Language Processing in Medical Applications
Srinivasan, V. P., Rosero, E., Sengottuvelan, P., Singhal, A., Singh, C., & Mayakannan, S. (2024) · doi:10.1002/9781394242528.ch15 · Cited by 2 · see citing works
Elbiach, O., Grissette, H., & Nfaoui, E. H. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280478 · Cited by 1 · see citing works
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Livieratos, A., Lin, J., Chasani, P., Gaga, M., Fousekis, F. S., Gogos, C. A., Akinosoglou, K., Katsanos, K. H., & Gamalo, M. (2026) · Methods and Protocols · doi:10.3390/mps9030069 · Cited by 1 · see citing works
Colicchio, T. K., Osborne, J. D., do Rosario, C. V., Anand, A., Timkovich, N. A., Wyatt, M., & Cimino, J. J. (2023) · PubMed Central · Cited by 1 · see citing works
Joint Extraction of Clinical Entities and Relations Using Multi-head Selection Method
Fang, X., Song, Y., & Maeda, A. (2021) · International Conference on Asian Language Processing · doi:10.1109/ialp54817.2021.9675275 · Cited by 1 · see citing works
Silverman, G., Finzel, R., Heinz, M. V., Vasilakes, J., Solinsky, J. C., McEwan, R., Knoll, B. C., Tignanelli, C. J., Liu, H., Xu, H., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02255 · Cited by 1 · see citing works
Predicting Stroke and Mortality in Mitral Regurgitation: A Gradient Boosting ApproachPreprint
Zhou, J., Lee, S., Liu, Y., Liu, T., Tse, G., & Zhang, Q. (2021) · medRxiv · doi:10.1101/2021.01.04.21249215
Garcia, G. C., & Ladeira, M. (2021) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti52073.2021.9476560
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Vilela, G. D. B., Fileni, C. H. P., & Passos, R. P. (2020) · Revista CPAQV - Centro de Pesquisas Avançadas em Qualidade de Vida · doi:10.36692/cpaqv-v12n1-0
Fine-tuning Clinical Language Models to Identify Adverse Drug Events in Clinical Text (Preprint)Preprint
Kopacheva, E., Lincke, A., Henriksson, A., Dalianis, H., & Hammar, T. (2025) · doi:10.2196/preprints.71949
Schreier, O., Yazdani, A., Galdadas, I., Kabak, R., Gervasio, F. L., Mu, G., & Teodoro, D. (2025) · doi:10.2196/preprints.77732
Schreier, O., Yazdani, A., Galdadas, I., Kabak, R., Gervasio, F. L., Mu, G., & Teodoro, D. (2025) · medRxiv · doi:10.1101/2025.07.14.25331486
A contrastive dual attention framework for enhancing adverse drug event relations extraction
Kashtriya, P., & Singh, P. (2025) · Revista Cientifica de Sistemas e Informatica · doi:10.51252/rcsi.v5i2.968
Hasan, A., Wu, J., Nguyen, Q., Andres, S., Guellil, I., Zhang, H., Casey, A., Alex, B., Guthrie, B., & Wu, H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110747 · Cited by 2 · see citing works
Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration
Huang, J., Huang, Y., Zhang, C. H. I., Shao, J., & Zhang, C. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03535-9
Park, J., Kim, M., & Lee, S. (2025) · Pharmacophore · doi:10.51847/bgtpljj5yy
- Authors
- Yang, X., Bian, J., Fang, R., Bjarnadottir, R. I., Hogan, W. R., & Wu, Y.
- Affiliations
- University of Florida Health (US)
- University of Florida (US)
- Department of Family, Community and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, USA
- Corresponding author
- Wu, Yonghui
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-28
- Volume
- 27
- Issue
- 1
- Pages
- 65-72
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7489076
- OpenAlex
- W2970198438
- Cited by
- 74
- References
- 46
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 13 · 2023: 14 · 2022: 11 · 2021: 12 · 2020: 7 · 2019: 1
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Florida Clinical
- Translational Science Institute
- NIH National Center for Advancing Translational Sciences (UL1TR001427, NIA R21AG062884)
- NIH
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- adverse drug reactions, medical named entity recognition, recurrent convolutional neural network, gradient boosting, relation classification, clinical text mining, long short-term memory
- MeSH terms
- Deep Learning, Humans, Natural Language Processing, Information Storage and Retrieval, Neural Networks, Computer, Narration, Electronic Health Records, Drug-Related Side Effects and Adverse Reactions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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