Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23375, first published .
The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

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

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

Journals

  1. Xiong Y, Chen S, Chen Q, Yan J, Tang B. Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study. JMIR Medical Informatics 2020;8(12):e23357 View
  2. Yang X, He X, Zhang H, Ma Y, Bian J, Wu Y. Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models. JMIR Medical Informatics 2020;8(11):e19735 View
  3. Kades K, Sellner J, Koehler G, Full P, Lai T, Kleesiek J, Maier-Hein K. Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study. JMIR Medical Informatics 2021;9(2):e22795 View
  4. Li J, Zhang X, Zhou X. ALBERT-Based Self-Ensemble Model With Semisupervised Learning and Data Augmentation for Clinical Semantic Textual Similarity Calculation: Algorithm Validation Study. JMIR Medical Informatics 2021;9(1):e23086 View
  5. Ormerod M, Martínez del Rincón J, Devereux B. Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis. JMIR Medical Informatics 2021;9(5):e23099 View
  6. Liu J, Capurro D, Nguyen A, Verspoor K. “Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks. Journal of Biomedical Informatics 2022;133:104149 View
  7. Landolsi M, Hlaoua L, Ben Romdhane L. Information extraction from electronic medical documents: state of the art and future research directions. Knowledge and Information Systems 2023;65(2):463 View
  8. Kim Y, Kim J, Lee J, Jang M, Yum Y, Kim S, Shin U, Kim Y, Joo H, Song S. A pre-trained BERT for Korean medical natural language processing. Scientific Reports 2022;12(1) View
  9. Wang Y, Beck D, Baldwin T, Verspoor K. Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression. Transactions of the Association for Computational Linguistics 2022;10:680 View
  10. Chang D, Lin E, Brandt C, Taylor R. Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison. JMIR Medical Informatics 2021;9(11):e23101 View
  11. Gao Y, Dligach D, Christensen L, Tesch S, Laffin R, Xu D, Miller T, Uzuner O, Churpek M, Afshar M. A scoping review of publicly available language tasks in clinical natural language processing. Journal of the American Medical Informatics Association 2022;29(10):1797 View
  12. Yang X, Chen A, PourNejatian N, Shin H, Smith K, Parisien C, Compas C, Martin C, Costa A, Flores M, Zhang Y, Magoc T, Harle C, Lipori G, Mitchell D, Hogan W, Shenkman E, Bian J, Wu Y. A large language model for electronic health records. npj Digital Medicine 2022;5(1) View
  13. Chen Q, Rankine A, Peng Y, Aghaarabi E, Lu Z. Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study. JMIR Medical Informatics 2021;9(12):e27386 View
  14. Chiu C, Villena F, Martin K, Núñez F, Besa C, Dunstan J. Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish. Frontiers in Artificial Intelligence 2022;5 View
  15. Kalyan K, Rajasekharan A, Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. Journal of Biomedical Informatics 2022;126:103982 View
  16. Su Q, Cheng G, Huang J. A review of research on eligibility criteria for clinical trials. Clinical and Experimental Medicine 2023;23(6):1867 View
  17. Moradi M, Blagec K, Samwald M. Improving the Robustness and Accuracy of Intelligent Clinical Text Processing: Producing Noise for Data Augmentation. SSRN Electronic Journal 2022 View
  18. Wang Y, Tao S, Xie N, Yang H, Baldwin T, Verspoor K. Collective Human Opinions in Semantic Textual Similarity. Transactions of the Association for Computational Linguistics 2023;11:997 View
  19. Bacco L, Dell’Orletta F, Lai H, Merone M, Nissim M. A text style transfer system for reducing the physician–patient expertise gap: An analysis with automatic and human evaluations. Expert Systems with Applications 2023;233:120874 View
  20. Landolsi M, Hlaoua L, Romdhane L. Extracting and structuring information from the electronic medical text: state of the art and trendy directions. Multimedia Tools and Applications 2023;83(7):21229 View
  21. Kim E, Jeong Y, Choi M. MediBioDeBERTa: Biomedical Language Model With Continuous Learning and Intermediate Fine-Tuning. IEEE Access 2023;11:141036 View
  22. Caufield J, Hegde H, Emonet V, Harris N, Joachimiak M, Matentzoglu N, Kim H, Moxon S, Reese J, Haendel M, Robinson P, Mungall C, Wren J. Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning. Bioinformatics 2024;40(3) View
  23. 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. Kantor K, Morzy M. Artificial Intelligence in Medicine. View
  2. Dramé K, Diallo G, Sambe G. Web Information Systems and Technologies. View