  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.
