A Year of Papers Using Biomedical Texts:

Summary

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.

Keywords

Natural Language Processing - social media - state-of-the-art review - Artificial Intelligence

• References

• 1 Nadkarni PM, Ohno-Machado L, Chapman WW. Natural Language Processing: an introduction. J Am Med Inform Assoc 2011; 18: 544-51
• 2 Friedman C, Hripcsak G. Natural Language Processing and its future in medicine. Acad Med 1999; 74 (08) 890-5
• 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.
• 5 Zheng T, Gao Y, Wang F, Fan C, Fu X, Li M. et al. Detection of medical text semantic similarity based on convolutional neural network. BMC Med Inform Decis Mak 2019; 19 (01) 156
• 6 Lerner I, Paris N, Tannier X. Terminologies augmented recurrent neural network model for clinical named entity recognition. J Biomed Inform 2020; 102: 103356
• 7 Viani N, Miller TA, Napolitano C, Priori SG, Savova GK, Bellazzi R. et al Supervised methods to extract clinical events from cardiology reports in italian. J Biomed Inform 2019; 95: 103219
• 8 Santiso S, Pérez A, Casillas A. Smoothing dense spaces for improved relation extraction between drugs and adverse reactions. Int J Med Inform 2019; 128: 39-45
• 9 Lavertu A, Altman RB. Redmed: Extending drug lexicons for social media applications. J Biomed Inform 2019; 99: 103307
• 10 Hong N, Wen A, Stone DJ, Tsuji S, Kingsbury PR, Rasmusen LV. et al Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries. J Biomed Inform 2019; 99: 103310
• 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
• 15 Hahn U, Oleynik M. Medical information extraction in the age of deep learning: Methodological foundations and neural network applications. 2020 Yearb Med Inform (in press).
• 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
• 17 Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. J Am Med Inform Assoc 2019; 26 (11) 1297-304
• 18 Tulkens S, Šuster S, Daelemans W. Unsupervised concept extraction from clinical text through semantic composition. J Biomed Inform 2019; 91: 103120