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. 2022 Aug;31(1):243-253.
doi: 10.1055/s-0042-1742510. Epub 2022 Jun 2.

Natural Language Processing: from Bedside to Everywhere

Eiji Aramaki  1 , Shoko Wakamiya  1 , Shuntaro Yada  1 , Yuta Nakamura  2
Affiliations

Natural Language Processing: from Bedside to Everywhere

Eiji Aramaki et al. Yearb Med Inform. 2022 Aug.

Abstract

Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions.

Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas.

Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP.

Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Table 1
Table 1
Summary of bedside NLP application studies. BART = Bidirectional Auto-Regressive Transformer, BERT = Bidirectional Encoder Representations of Transformers, CNN = Convolutional Neural Network, EL = Entity Linking, GBDT = Gradient Boosting Decision Tree, LSTM = Long Short-Term Memory, NER = Named Entity Recognition, NN = Neural Network, RCT = Randomized Controlled Trial, RoBERTa = Robustly Optimized BERT Pretraining Approach, SVM = Support Vector Machine, T5 = Text-to-Text Transfer Transformer
Table 1 (continued)
Table 1 (continued)
Summary of bedside NLP application studies. BART = Bidirectional Auto-Regressive Transformer, BERT = Bidirectional Encoder Representations of Transformers, CNN = Convolutional Neural Network, EL = Entity Linking, GBDT = Gradient Boosting Decision Tree, LSTM = Long Short-Term Memory, NER = Named Entity Recognition, NN = Neural Network, RCT = Randomized Controlled Trial, RoBERTa = Robustly Optimized BERT Pretraining Approach, SVM = Support Vector Machine, T5 = Text-to-Text Transfer Transformer
Fig. 1
Fig. 1
Different task formulations for the same task (a case of the adverse drug event task).
See this image and copyright information in PMC

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