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A Year of Papers Using Biomedical Texts:Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook
21 August 2020 (online)
Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.
Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019.
Results: Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation.
Conclusions: The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.
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- 3 Guan J, Li R, Yu S, Zhang X. A method for generating synthetic electronic medical record text. IEEE/ACM Trans Comput Biol Bioinform 2019.
- 4 Chen Y, Zhou C, Li T, Wu H, Zhao X, Ye K. et al. Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training. J Biomed Inform 2019 Aug;96:103252.
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- 11 Sterling NW, Patzer RE, Di M, Schrager JD. Prediction of emergency department patient disposition based on natural language processing of triage notes. Int J Med Inform 2019; 129: 184-8
- 12 Liu F, Pradhan R, Druhl E, Freund E, Liu W, Sauer BC. et al. Learning to detect and understand drug discontinuation events from clinical narratives. J Am Med Inform Assoc 2019; 26 (10) 943-51
- 13 Khadanga S, Aggrawa K, Joty S, Srivastava J. Using clinical notes with time series data for ICU management. In ACL editor. Proceedings of EMNLP 2019. Hongkong, China; 2019. p. 6431–6.
- 14 Rosemblat G, Fiszman M, Shin D, Kilicoglu H. Towards a characterization of apparent contradictions in the biomedical literature using context analysis. J Biomed Inform 2019; 98: 103275
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- 16 Lee J, Yoon W, Kim S, Kim D, Kim S, Ho So C. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2019; 36 (04) 1234-40
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