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Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review

arXiv 2304.02768 ↗First posted 5 Apr 2023cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

The combined growth of available data and their unstructured nature has received increased interest in natural language processing (NLP) techniques to make value of these data assets since this format is not suitable for statistical analysis. This work presents a systematic literature review of state-of-the-art advances using transformer-based methods on electronic medical records (EMRs) in different NLP tasks. To the best of our knowledge, this work is unique in providing a comprehensive review of research on transformer-based methods for NLP applied to the EMR field. In the initial query, 99 articles were selected from three public databases and filtered into 65 articles for detailed analysis. The papers were analyzed with respect to the business problem, NLP task, models and techniques, availability of datasets, reproducibility of modeling, language, and exchange format. The paper presents some limitations of current research and some recommendations for further research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 95 references that are in the map. First posted on arXiv 5 Apr 2023.

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10 of 95 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
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  5. 2019BEHRT: Transformer for Electronic Health Recordscited by 263
  6. 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
  7. 2019TAPER: Time-Aware Patient EHR Representationcited by 22
  8. 2021Estimating Redundancy in Clinical Textcited by 10
  9. 2020Patient Cohort Retrieval using Transformer Language Modelscited by 7
  10. 2020Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distancecited by 6

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