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