MedSTS: a resource for clinical semantic textual similarity

WordNet: a lexical database for English

Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · see citing works

A vector space model for automatic indexing

Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521 · see citing works

VII. Note on regression and inheritance in the case of two parents

Pearson, K. (1895) · Proceedings of the Royal Society of London · doi:10.1098/rspl.1895.0041 · Cited by 3,473 · see citing works

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

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

Development and application of a metric on semantic nets

Rada, R., Mili, H., Bicknell, E. J., & Blettner, M. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.24528 · Cited by 1,835 · 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

Extracting information from textual documents in the electronic health record: a review of recent research

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

WordNet: similarity - measuring the relatedness of concepts

Pedersen, T., Patwardhan, S., & Michelizzi, J. (2004) · Cited by 854 · see citing works

WordNet::Similarity

Pedersen, T., Patwardhan, S., & Michelizzi, J. (2004) · doi:10.3115/1614025.1614037 · Cited by 829 · see citing works

Sentence similarity based on semantic nets and corpus statistics

Li, Y., McLean, D., Bandar, Z. A., O'Shea, J. D., & Crockett, K. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.130 · Cited by 819 · see citing works

OntoNotes: the 90% solution

Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., & Weischedel, R. (2006) · Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX - NAACL '06 · doi:10.3115/1614049.1614064 · Cited by 806 · see citing works

The Oxford handbook of computational linguistics

Mitkov, R. (2003) · Choice Reviews Online · doi:10.5860/choice.41-0758 · Cited by 718 · see citing works

SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity

Agirre, E., Cer, D. M., Diab, M., & González-Agirre, A. (2012) · Cited by 653 · see citing works

Measures of semantic similarity and relatedness in the biomedical domain

Ted Pedersen, A., Pakhomov, S., Patwardhan, S., & Chute, C. G. (2006) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2006.06.004 · Cited by 566 · see citing works

Types and Origins of Diagnostic Errors in Primary Care Settings

Singh, H., Giardina, T. D., Meyer, A. N. D., Forjuoh, S. N., Reis, M. D., & Thomas, E. J. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.2777 · Cited by 559 · see citing works

Collecting Image Annotations Using Amazon's Mechanical Turk

Rashtchian, C., Young, P., Hodosh, M., & Hockenmaier, J. (2010) · Cited by 541 · see citing works

SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

Agirre, E., Banea, C., Cer, D. M., Diab, M., González-Agirre, A., Mihalcea, R. F., Rigau, G., & Wiebe, J. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1081 · Cited by 527 · see citing works

Using Measures of Semantic Relatedness for Word Sense Disambiguation

Patwardhan, S., Banerjee, S., & Pedersen, T. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36456-0_24 · Cited by 500 · see citing works

Semantic text similarity using corpus-based word similarity and string similarity

Islam, A., & Inkpen, D. Z. (2008) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1376815.1376819 · Cited by 492 · see citing works

SemEval-2014 Task 10: Multilingual Semantic Textual Similarity

Agirre, E., Banea, C., Cardie, C., Cer, D. M., Diab, M., González-Agirre, A., Guo, W., Mihalcea, R. F., Rigau, G., & Wiebe, J. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2010 · Cited by 492 · see citing works

SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

Agirre, E., Banea, C., Cardie, C., Cer, D. M., Diab, M., González-Agirre, A., Guo, W., López-Gazpio, I., Maritxalar, M., Mihalcea, R. F., et al. (2015) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s15-2045 · Cited by 488 · see citing works

Sentence Fusion for Multidocument News Summarization

Barzilay, R., & McKeown, K. R. (2005) · Computational Linguistics · doi:10.1162/089120105774321091 · Cited by 429 · see citing works

A comparison of word embeddings for the biomedical natural language processing

Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.09.008 · Cited by 370 · see citing works

*SEM 2013 shared task: Semantic Textual Similarity

Agirre, E., Cer, D. M., Diab, M., González-Agirre, A., & Guo, W. (2013) · Joint Conference on Lexical and Computational Semantics · Cited by 360 · see citing works

Measuring the semantic similarity of texts

Corley, C. D., & Mihalcea, R. F. (2005) · doi:10.3115/1631862.1631865 · Cited by 357 · see citing works

Clinical Documentation in the 21st Century: Executive Summary of a Policy Position Paper From the American College of Physicians

Kuhn, T., Basch, P., Barr, M., & Yackel, T. R. (2015) · Annals of Internal Medicine · doi:10.7326/m14-2128 · Cited by 272 · see citing works

A new sentence similarity measure and sentence based extractive technique for automatic text summarization

Aliguliyev, R. M. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.11.022 · Cited by 265 · see citing works

Can Electronic Clinical Documentation Help Prevent Diagnostic Errors?

Schiff, G. D., & Bates, D. W. (2010) · New England Journal of Medicine · doi:10.1056/nejmp0911734 · Cited by 239 · see citing works

Automated methods for the summarization of electronic health records

Pivovarov, R., & Elhadad, N. (2015) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocv032 · Cited by 238 · see citing works

Yearbook of Medical Informatics

(2013) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638865 · Cited by 237 · see citing works

Paraphrasing for automatic evaluation

Kauchak, D., & Barzilay, R. (2006) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1220835.1220893 · Cited by 222 · see citing works

SemEval-2014 Task 7: Analysis of Clinical Text

Pradhan, S., Elhadad, N., Chapman, W. W., Manandhar, S., & Savova, G. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2007 · Cited by 208 · see citing works

UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

Bär, D., Biemann, C., Gurevych, I., & Zesch, T. (2012) · TUbilio (Technical University of Darmstadt) · doi:10.17185/duepublico/72186 · Cited by 200 · see citing works

TakeLab: Systems for Measuring Semantic Text Similarity

Šarić, F., Glavaš, G., Karan, M., Šnajder, J., & Bašić, B. D. (2012) · MADOC (University of Mannheim) · Cited by 200 · see citing works

From The Office Of The National Coordinator: The Strategy For Advancing The Exchange Of Health Information

Williams, C., Mostashari, F., Mertz, K., Hogin, E., & Atwal, P. M. S. (2012) · Health Affairs · doi:10.1377/hlthaff.2011.1314 · Cited by 183 · see citing works

Implementation of the Federal Health Information Technology Initiative

Blumenthal, D. (2011) · New England Journal of Medicine · doi:10.1056/nejmsr1112158 · Cited by 177 · see citing works

Quantifying clinical narrative redundancy in an electronic health record

Wrenn, J. O., Stein, D. M., Bakken, S., & Stetson, P. D. (2010) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3390 · Cited by 153 · see citing works

Developing a corpus of plagiarised short answers

Clough, P. D., & Stevenson, M. (2010) · Language Resources and Evaluation · doi:10.1007/s10579-009-9112-1 · Cited by 139 · see citing works

HARVEST, a longitudinal patient record summarizer

Hirsch, J. S., Tanenbaum, J. S., Lipsky Gorman, S., Liu, C., Schmitz, E., Hashorva, D., Ervits, A., Vawdrey, D., Sturm, M., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002945 · Cited by 135 · see citing works

Intelligent Indexing and Semantic Retrieval of Multimodal Documents

Srihari, R. K., Zhang, Z. M., & Rao, A. (2000) · Information Retrieval · doi:10.1023/a:1009962928226 · Cited by 120 · see citing works

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Blanco‐Fernández, Y., Pazos-Arias, J. J., Gil‐Solla, A., Ramos‐Cabrer, M., López‐Nores, M., García‐Duque, J., Vilas, A. F., Redondo, R. P. D., & Muñoz, J. B. (2007) · Knowledge-Based Systems · doi:10.1016/j.knosys.2007.07.004 · Cited by 111 · see citing works

Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media

Guo, W., Li, H., Ji, H., & Diab, M. (2013) · Cited by 102 · see citing works

Semantic Textual Similarity Methods, Tools, and Applications: A Survey

Majumder, G., Pakray, P., Gelbukh, A. F., & Pinto, D. (2016) · Computación y Sistemas · doi:10.13053/cys-20-4-2506 · Cited by 88 · see citing works

Characterizing the Source of Text in Electronic Health Record Progress Notes

Wang, M. D., Khanna, R. R., & Najafi, N. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.1548 · Cited by 85 · see citing works

Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis

Wu, S. T., Liu, H., Li, D., Tao, C., Musen, M. A., Chute, C. G., & Shah, N. H. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000744 · Cited by 80 · see citing works

Computerized provider documentation: findings and implications of a multisite study of clinicians and administrators: Table 1

Embi, P. J., Weir, C., Efthimiadis, E. N., Thielke, S. M., Hedeen, A. N., & Hammond, K. W. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000946 · Cited by 74 · see citing works

Assessing sentence similarity through lexical, syntactic and semantic analysis

Ferreira, R., Lins, R. D., Simske, S. J., Freitas, F., & Riss, M. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2016.01.003 · Cited by 60 · see citing works

Natural Language Processing in Health Care and Biomedicine

Friedman, C., & Elhadad, N. (2013) · Springer eBooks · doi:10.1007/978-1-4471-4474-8_8 · Cited by 60 · see citing works

Evaluating measures of redundancy in clinical texts.

Zhang, R., Pakhomov, S., McInnes, B. T., & Melton, G. B. (2011) · PubMed Central · Cited by 47 · see citing works

Beetle II: A System for Tutoring and Computational Linguistics Experimentation

Dzikovska, M. O., Moore, J. D., Steinhauser, N. B., Campbell, G. E., Farrow, E., & Callaway, C. B. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 46 · see citing works

A knowledge-based question answering system for B2C eCommerce

Tapeh, A. G., & Rahgozar, M. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.04.005 · Cited by 43 · see citing works

The Oxford Handbook of Computational Linguistics

Sampson, G. (2003) · Literary and Linguistic Computing · doi:10.1093/llc/18.3.333 · Cited by 33 · see citing works

Discovering implicit intention-level knowledge from natural-language texts

Atkinson, J., Cabrera, A. F., & Aravena, E. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.10.007 · Cited by 29 · see citing works

A Comparison of Word Embeddings for the Biomedical Natural Language ProcessingPreprint

Wang, Y., Liu, S., Afzal, N., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00400 · Cited by 28 · see citing works

MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model

Afzal, N., Wang, Y., & Liu, H. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1103 · Cited by 26 · see citing works

BioCreative/OHNLP Challenge 2018

Rastegar-Mojarad, M., Liu, S., Wang, Y., Afzal, N., Wang, L., Shen, F., Fu, S., & Liu, H. (2018) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3233547.3233672 · Cited by 26 · see citing works

Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts

Wang, Y., Rastegar-Mojarad, M., Komandur-Elayavilli, R., & Liu, H. (2017) · Database · doi:10.1093/database/bax091 · Cited by 18 · see citing works

Learning Document Semantic Representation with Hybrid Deep Belief Network

Yan, Y., Yin, X.-C., Li, S., Yang, M., & Hao, H. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/650527 · Cited by 16 · see citing works

Using language models to identify relevant new information in inpatient clinical notes.

Zhang, R., Pakhomov, S., Lee, J. T., & Melton, G. B. (2014) · PubMed Central · Cited by 13 · see citing works

A frequency-filtering strategy of obtaining PHI-free sentences from clinical data repository

Li, D., Rastegar-Mojarad, M., Elayavilli, R. K., Wang, Y., Mehrabi, S., Yu, Y., Sohn, S., Li, Y., Afzal, N., & Liu, H. (2015) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2808719.2808752 · Cited by 9 · see citing works

Exploiting Sentence Similarities for Better Alignments

Li, T., & Srikumar, V. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1237 · Cited by 8 · see citing works

Medical concept intersection between outside medical records and consultant notes: A case study in transferred cardiovascular patients

Moon, S., Liu, S., Kingsbury, P., Chen, D. J., Wang, Y., Shen, F., Chaudhry, R., & Liu, H. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217883 · Cited by 5 · see citing works

Semantic Indexing of Multilingual Corpora and its Application on the History Domain

Raganato, A., Camacho-Collados, J., Raganato, A., & Joung, Y. (2016) · BOA (University of Milano-Bicocca) · Cited by 2 · see citing works

Analysis of the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity

Magnolini, S., Vo, N. P. A., & Popescu, O. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49130-1_33 · Cited by 1 · see citing works

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

Wieting, J., & Gimpel, K. (2017) · arXiv · doi:10.48550/arxiv.1705.00364

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

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Peng, Y., Yan, S., & Lu, Z. (2019) · Proceedings of the 18th BioNLP Workshop and Shared Task · doi:10.18653/v1/w19-5006 · Cited by 899 · see citing works

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Junaid, S. B., Imam, A. A., Balogun, A. O., Silva, L. C. D., Surakat, Y. A., Kumar, G., Abdulkarim, M., Shuaibu, A. N., Garba, A., Sahalu, Y., et al. (2022) · Healthcare · doi:10.3390/healthcare10101940 · Cited by 398 · see citing works

Large language models in medical and healthcare fields: applications, advances, and challenges

Wang, D., & Zhang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10921-0 · Cited by 246 · see citing works

BioSentVec: creating sentence embeddings for biomedical texts

Chen, Q.-Y., Peng, Y., & Lu, Z.-Y. (2018) · IEEE International Conference on Healthcare Informatics · doi:10.1109/ichi.2019.8904728 · Cited by 241 · see citing works

Transformers and large language models in healthcare: A review

Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., Bihorac, A., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102900 · Cited by 243 · see citing works

MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval

Jin, Q., Kim, W., Chen, Q., Comeau, D. C., Yeganova, L., Wilbur, W. J., & Lu, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad651 · Cited by 175 · see citing works

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying

Soroush, A., Glicksberg, B. S., Zimlichman, E., Barash, Y., Freeman, R., Charney, A. W., Nadkarni, G. N., & Klang, E. (2024) · NEJM AI · doi:10.1056/aidbp2300040 · Cited by 137 · see citing works

CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters

Boukkouri, H. E., Ferret, O., Lavergne, T., Noji, H., Zweigenbaum, P., & Tsujii, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.609 · Cited by 152 · see citing works

An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining

Peng, Y., Chen, Q., & Lu, Z. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.22 · Cited by 112 · see citing works

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Kumar, G., Basri, S. B., Imam, A. A., Khowaja, S. A., Capretz, L. F., & Balogun, A. O. (2021) · Applied Sciences · doi:10.3390/app11178275 · Cited by 91 · see citing works

Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation

Elangovan, A., He, J., & Verspoor, K. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.113 · Cited by 75 · see citing works

Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Chen, P., Wang, S.-M., Liao, W.-C., Kuo, L.-C., Chen, K., Lin, Y., Yang, C.-Y., Chiu, C.-H., Chang, S.-C., & Lai, F. (2021) · JMIR Medical Informatics · doi:10.2196/23230 · Cited by 71 · see citing works

Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT

Naseem, U., Dunn, A. G., Khushi, M., & Kim, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04688-w · Cited by 68 · see citing works

Biolord-2023: Semantic textual representations fusing large language models and clinical knowledge graph insights

Remy, F., Demuynck, K., & Demeester, T. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae029 · Cited by 64 · see citing works

A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi – LSTM model for semantic text similarity identification

Viji, D., & Revathy, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11771-6 · Cited by 63 · see citing works

A pre-trained BERT for Korean medical natural language processing

Kim, Y., Kim, J.-H., Lee, J. M., Jang, M. J., Yum, Y., Kim, S., Shin, U., Kim, Y.-M., Joo, H. J., & Song, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-17806-8 · Cited by 63 · see citing works

The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

Wang, Y., Fu, S., Shen, F., Henry, S., Uzuner, Ö., & Liu, H. (2020) · JMIR Medical Informatics · doi:10.2196/23375 · Cited by 60 · see citing works

Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models

Yang, X., He, X., Zhang, H., Ma, Y., Bian, J., & Wu, Y. (2020) · JMIR Medical Informatics · doi:10.2196/19735 · Cited by 57 · see citing works

Differential Privacy for Text Analytics via Natural Text Sanitization

Yue, X., Du, M., Wang, T., Li, Y., Sun, H., & Chow, S. S. M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.337 · Cited by 65 · see citing works

Pre-trained language models in medicine: A survey

Luo, X., Deng, Z., Yang, B., & Luo, M. Y. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102904 · Cited by 51 · see citing works

Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes

Gangavarapu, T., Jayasimha, A., Krishnan, G. S., & Kamath, S. S. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105321 · Cited by 40 · see citing works

Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis

Ormerod, M., del Rincón, J. M., & Devereux, B. J. (2021) · JMIR Medical Informatics · doi:10.2196/23099 · Cited by 40 · see citing works

A deep database of medical abbreviations and acronyms for natural language processing

Liu, L. G., Grossman, R. H., Mitchell, E. G., Weng, C., Natarajan, K., Hripcsak, G. M., & Vawdrey, D. K. (2021) · Scientific Data · doi:10.1038/s41597-021-00929-4 · Cited by 39 · see citing works

A survey for large language models in biomedicine

Wang, C., Li, M., He, J., Wang, Z., Darzidehkalani, E., Chen, Z., Ye, J., Li, T., Su, Y., Ke, J., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103268 · Cited by 51 · see citing works

Review: Privacy-Preservation in the Context of Natural Language Processing

Mahendran, D., Luo, C., & McInnes, B. T. (2021) · IEEE Access · doi:10.1109/access.2021.3124163 · Cited by 37 · see citing works

GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainPreprint

Moradi, M., Blagec, K., Haberl, F., & Matthias Samwald, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.02555 · Cited by 37 · see citing works

Pre-trained Language Models in Biomedical Domain: A Systematic SurveyPreprint

Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05006 · Cited by 34 · see citing works

Evaluating sentence representations for biomedical text: Methods and experimental results

Tawfik, N. S., & Spruit, M. R. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103396 · Cited by 34 · see citing works

MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

Shao, H., Ma, E., Zhu, M. F., Deng, X., & Zhai, S. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.036323 · Cited by 31 · see citing works

A Customized Text Sanitization Mechanism with Differential Privacy

Chen, S., Mo, F., Wang, Y., Chen, C., Nie, J., Wang, C., & Cui, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.355 · Cited by 39 · see citing works

Semantic Textual Similarity in Japanese Clinical Domain Texts Using BERT

Mutinda, F. W., Yada, S., Wakamiya, S., & Aramaki, E. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1731390 · Cited by 30 · see citing works

Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning

Mahajan, D., Poddar, A., Liang, J. J., Lin, Y.-T. R., Prager, J. M., Suryanarayanan, P., Raghavan, P., & Tsou, C.-H. (2020) · JMIR Medical Informatics · doi:10.2196/22508 · Cited by 29 · see citing works

CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From CharactersPreprint

Boukkouri, H. E., Ferret, O., Lavergne, T., Noji, H., Zweigenbaum, P., & Tsujii, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10392 · Cited by 26 · see citing works

GatorTron: 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

Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study

Kades, K., Sellner, J., Koehler, G., Full, P. M., Lai, T. Y. E., Kleesiek, J., & Maier-Hein, K. H. (2020) · JMIR Medical Informatics · doi:10.2196/22795 · Cited by 24 · see citing works

Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression

Wang, Y., Beck, D., Baldwin, T., & Verspoor, K. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00483 · Cited by 23 · see citing works

Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations

Koroleva, A., Kamath, S., & Paroubek, P. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.yjbinx.2019.100058 · Cited by 23 · see citing works

A Novel Approach for Semantic Extractive Text Summarization

Waseemullah, W., Fatima, Z., Zardari, S., Fahim, M., Siddiqui, M. A., Ibrahim, A. A. A., Nisar, K., & Naz, L. F. (2022) · Applied Sciences · doi:10.3390/app12094479 · Cited by 23 · see citing works

Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records

Chen, Q., Du, J., Kim, S., Wilbur, W. J., & Lu, Z. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1044-0 · Cited by 19 · see citing works

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Shen, F., Liu, S., Fu, S., Wang, Y., Henry, S., Uzuner, O., & Liu, H. (2021) · JMIR Medical Informatics · doi:10.2196/24008 · Cited by 21 · see citing works

Learning from Unlabelled Data for Clinical Semantic Textual Similarity

Wang, Y., Verspoor, K. M., & Baldwin, T. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.25 · Cited by 20 · see citing works

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT

Wada, S., Takeda, T., Okada, K., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102889 · Cited by 24 · see citing works

Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

Chen, Q., Rankine, A., Peng, Y., Aghaarabi, E., & Lu, Z. (2021) · JMIR Medical Informatics · doi:10.2196/27386 · Cited by 21 · see citing works

A Pre-Training Technique to Localize Medical BERT and to Enhance Biomedical BERTPreprint

Wada, S., Takeda, T., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2020) · doi:10.21203/rs.3.rs-103477/v1 · Cited by 19 · see citing works

Estimating redundancy in clinical text

Searle, T., Ibrahim, Z. M., Teo, J., & Dobson, R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103938 · Cited by 18 · see citing works

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

(2021) · doi:10.18653/v1/2021.naacl-demos · Cited by 18 · see citing works

Quality assessment of functional status documentation in EHRs across different healthcare institutions

Fu, S., Vassilaki, M., Ibrahim, O. A., Petersen, R. C., Pagali, S., St Sauver, J., Moon, S., Wang, L., Fan, J. W., Liu, H., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.958539 · Cited by 15 · see citing works

The Potential of AI in Care Optimization: Insights from the User-Driven Co-Development of a Care Integration System

Schneider‐Kamp, A. (2021) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580211017992 · Cited by 17 · see citing works

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages

Akbasli, I. T., Bırbılen, A. Z., & Tekşam, Ö. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02871-6 · Cited by 17 · see citing works

Improving the robustness and accuracy of biomedical language models through adversarial training

Moradi, M., & Samwald, M. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104114 · Cited by 18 · see citing works

Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison

Chang, D., Lin, E. C., Brandt, C., & Taylor, R. A. (2021) · JMIR Medical Informatics · doi:10.2196/23101 · Cited by 16 · see citing works

Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity

Wang, Y., Liu, F., Verspoor, K. M., & Baldwin, T. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.11 · Cited by 16 · see citing works

BioLORD: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions

Remy, F., Demuynck, K., & Demeester, T. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.104 · Cited by 15 · see citing works

Pre-training technique to localize medical BERT and enhance biomedical BERTPreprint

Wada, S., Takeda, T., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07202 · Cited by 20 · see citing works

Semantic Textual Similarity Analysis of Clinical Text in the Era of LLM

Feng, Y. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00227 · Cited by 14 · see citing works

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

Frisoni, G., Moro, G., Carlassare, G., & Carbonaro, A. (2021) · Sensors · doi:10.3390/s22010003 · Cited by 14 · see citing works

TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes

Gangavarapu, T., Jayasimha, A., Krishnan, G. S., & Kamath, S. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23281-8_16 · Cited by 14 · see citing works

Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

Xiong, Y., Chen, S., Chen, Q., Yan, J., & Tang, B. (2020) · JMIR Medical Informatics · doi:10.2196/23357 · Cited by 11 · see citing works

Quantifying semantic similarity of clinical evidence in the biomedical literature to facilitate related evidence synthesis

Hassanzadeh, H., Nguyen, A. N., & Verspoor, K. M. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103321 · Cited by 11 · see citing works

A Comparative Analysis of Temporal Long Text Similarity: Application to Financial Documents

Rawte, V., Gupta, A., & Zaki, M. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-66981-2_7 · Cited by 11 · see citing works

Selected articles from the BioCreative/OHNLP challenge 2018

Liu, S., Wang, Y., & Liu, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0994-6 · Cited by 10 · see citing works

Querying Across Genres for Medical Claims in News

Zuo, C., Acharya, N., & Banerjee, R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.139 · Cited by 10 · see citing works

Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts

Atabuzzaman, M., Shajalal, M., Ahmed, M. E., Afjal, M. I., & Aono, M. (2021) · IEEE Access · doi:10.1109/access.2021.3074747 · Cited by 10 · see citing works

A text style transfer system for reducing the physician–patient expertise gap: An analysis with automatic and human evaluations

Bacco, L., Dell’Orletta⋄, F., Lai, H., Merone, M., & Nissim, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120874 · Cited by 9 · see citing works

MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information RetrievalPreprint

Jin, Q., Kim, W., Chen, Q., Comeau, D. C., Yeganova, L. E., Wilbur, W. J., & Lu, Z. (2023) · PubMed Central · doi:10.48550/arxiv.2307.00589 · Cited by 9 · see citing works

Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings

Antunes, R., Silva, J., & Matos, S. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373987 · Cited by 8 · see citing works

Knowledge-Guided Efficient Representation Learning for Biomedical Domain

Jha, K., Xun, G., Du, N., & Zhang, A. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467118 · Cited by 7 · see citing works

Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts

Remy, F., Scaboro, S., & Portelli, B. (2023) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2023.socialnlp-1.6 · Cited by 7 · see citing works

LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery

Yeganova, L. E., Kim, W. B., Tian, S., Comeau, D. C., Wilbur, W. J., & Lu, Z. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf417 · Cited by 7 · see citing works

Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports

Zlabinger, M., Sabou, M., Hofstätter, S., & Hanbury, A. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.274 · Cited by 7 · see citing works

Comparing large language models and text embedding models for automated classification of textual, semantic, and critical changes in radiology reports

Lindholz, M., Burdenski, A., Ruppel, R. R., Schulze‐Weddige, S., Baumgärtner, G. L., Schobert, I. T., Haack, A.-M., Eminovic, S., Milnik, A., Hamm, C. A., et al. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112316 · Cited by 6 · see citing works

Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies

Lertvittayakumjorn, P., Petej, I., Gao, Y., Krishnamurthy, Y., van der Gaag, A., Jago, R., & Stathis, K. (2021) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2021.acl-demo.10 · Cited by 6 · see citing works

HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

Lastra-Díaz, J. J., Lara-Clares, A., & García-Serrano, A. M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-021-04539-0 · Cited by 6 · see citing works

EARA: Improving Biomedical Semantic Textual Similarity with Entity-Aligned Attention and Retrieval Augmentation

Xiong, Y., Yang, X., Liu, L., Wong, K., Chen, Q., Xiang, Y., & Tang, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.586 · Cited by 4 · see citing works

A Study of the State of the Art Approaches and Datasets for Multilingual Natural Language Inference

Renjit, S., & Idicula, S. M. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11673-2 · Cited by 5 · see citing works

EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain

Rahman, M. A., Preum, S. M., Williams, R. D., Alemzadeh, H., & Stankovic, J. A. (2023) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3580252.3586978 · Cited by 5 · see citing works

Automatic consistency assurance for literature-based gene ontology annotation

Chen, J., Geard, N. L., Zobel, J., & Verspoor, K. M. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04479-9 · Cited by 5 · see citing works

Beyond belief: a cross-genre study on perception and validation of health information online

Zuo, C., Mathur, K., Kela, D., Faramarzi, N. S., & Banerjee, R. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00310-7 · Cited by 5 · see citing works

Compositional Zero-Shot Domain Transfer with Text-to-Text Models

Liu, F., Liu, Q., Bannur, S., Pérez‐García, F., Usuyama, N., Zhang, S., Naumann, T., Nori, A., Poon, H., Alvarez-Valle, J., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00585 · Cited by 5 · see citing works

A survey on large language models from general purpose to medical applications: Datasets, methodologies, and evaluations

Wang, J., Ning, H., Peng, Y., Wei, Q., Tesfai, D., Mao, W., Zhu, T., & Huang, R. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132914 · Cited by 11 · see citing works

Efficient multi-task learning with instance selection for biomedical NLP

Bonfigli, A., Bacco, L., Pecchia, L., Merone, M., & Dell’Orletta⋄, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110050 · Cited by 4 · see citing works

Coherence-based Modeling of Clinical Concepts Inferred from Heterogeneous Clinical Notes for ICU Patient Risk Stratification

Gangavarapu, T., Krishnan, G. S., & Kamath, S. S. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1095 · Cited by 4 · see citing works

Protocol for a reproducible experimental survey on biomedical sentence similarity

Lara-Clares, A., Lastra-Díaz, J. J., & García-Serrano, A. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248663 · Cited by 4 · see citing works

Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation

Han, J., & Yang, L. (2024) · Mathematics · doi:10.3390/math12243990 · Cited by 3 · see citing works

Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis

Jo, E., Cho, E., Lee, Y., Song, S., & Joo, H. J. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03262-7 · Cited by 4 · see citing works

SUMEX: A hybrid framework for Semantic textUal siMilarity and EXplanation generation

Saeed, S., Rajput, Q., & Haider, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103771 · Cited by 5 · see citing works

Comparative Study and Evaluation of Machine Learning Models for Semantic Textual Similarity

Sasoko, W. H., Setyanto, A., Kusrini, K., & Martínez‐Béjar, R. (2024) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee63424.2024.10730053 · Cited by 3 · see citing works

Semantic similarity-based credit attribution on citation paths: a method for allocating residual citation to and investigating depth of influence of scientific communications

Asubiaro, T., & Ajiferuke, I. (2022) · Scientometrics · doi:10.1007/s11192-022-04522-3 · Cited by 3 · see citing works

Pemodelan Text Mining dalam Pengkodean Penyakit Pasien Berdasar Kode ICD 10

Parjono, P., & Kusumadewi, S. (2023) · Jurnal Nasional Teknologi dan Sistem Informasi · doi:10.25077/teknosi.v9i2.2023.200-207 · Cited by 3 · see citing works

Evaluation of Five Sentence Similarity Models on Electronic Medical Records

Chen, Q., Du, J., Kim, S., Wilbur, W. J., & Lu, Z. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3343239 · Cited by 3 · see citing works

Multifaceted Domain-Specific Document Embeddings

Risch, J., Hager, P., & Krestel, R. (2021) · doi:10.18653/v1/2021.naacl-demos.9 · Cited by 3 · see citing works

Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics

Ghajari, A., Benito-Santos, A., Ros, S., Fresno, V., & González‐Blanco, E. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113549 · Cited by 2 · see citing works

Textual similarity calculation techniques in the medical field: a retrospective review

Cui, H., Wang, S., Ma, H., Zhu, X., Zhang, L., Piao, M., & Peng, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06634-8 · Cited by 2 · see citing works

Combining Attention-based Models with the MeSH Ontology for Semantic Textual Similarity in Clinical Notes

Faramarzi, N. S., Dara, A., & Banerjee, R. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00023 · Cited by 2 · see citing works

Collective Human Opinions in Semantic Textual Similarity

Wang, Y., Tao, S., Xie, N., Yang, H., Baldwin, T., & Verspoor, K. M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00584 · Cited by 3 · see citing works

Intrinsic and Extrinsic Evaluation of the Quality of Biomedical Embeddings in Different Languages

Franceschini, P. M., dos Santos, H. D. P., & Vieira, R. L. R. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00058 · Cited by 2 · see citing works

Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models

Yanaka, H., Nakamura, Y., Chida, Y., & Kurosawa, T. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.2 · Cited by 2 · see citing works

Natural Language Transfer Learning for Physiological Textual Similarity

Awatramani, V., & Gupta, P. (2020) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence47617.2020.9058216 · Cited by 2 · see citing works

Over-Sampling Effect in Pre-Training for Bidirectional Encoder Representations from Transformers (BERT) to Localize Medical BERT and Enhance Biomedical BERT (Preprint)Preprint

Wada, S., Takeda, T., Okada, K., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2022) · doi:10.2196/preprints.40992 · Cited by 2 · see citing works

The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

Wang, Y., Fu, S., Shen, F., Henry, S., Uzuner, Ö., & Liu, H. (2020) · JMIR Medical Informatics · doi:10.2196/23375 · Cited by 60 · see citing works

Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison

Chang, D., Lin, E. C., Brandt, C., & Taylor, R. A. (2021) · JMIR Medical Informatics · doi:10.2196/23101 · Cited by 16 · see citing works

Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning

Mahajan, D., Poddar, A., Liang, J. J., Lin, Y.-T. R., Prager, J. M., Suryanarayanan, P., Raghavan, P., & Tsou, C.-H. (2020) · JMIR Medical Informatics · doi:10.2196/22508 · Cited by 29 · see citing works

Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models

Yang, X., He, X., Zhang, H., Ma, Y., Bian, J., & Wu, Y. (2020) · JMIR Medical Informatics · doi:10.2196/19735 · Cited by 57 · see citing works

Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings

Antunes, R., Silva, J., & Matos, S. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373987 · Cited by 8 · see citing works