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

Preprints (earlier versions) of this paper are available at, 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


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  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
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  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
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  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
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  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
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  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
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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