  Background: Natural Language Processing (NLP) is widely used to extract
clinical insights from Electronic Health Records (EHRs). However, the lack of
annotated data, automated tools, and other challenges hinder the full
utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL)
and NLP techniques are studied and compared to understand the limitations and
opportunities in this space comprehensively.
  Methodology: After screening 261 articles from 11 databases, we included 127
papers for full-text review covering seven categories of articles: 1) medical
note classification, 2) clinical entity recognition, 3) text summarisation, 4)
deep learning (DL) and transfer learning architecture, 5) information
extraction, 6) Medical language translation and 7) other NLP applications. This
study follows the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) guidelines.
  Result and Discussion: EHR was the most commonly used data type among the
selected articles, and the datasets were primarily unstructured. Various ML and
DL methods were used, with prediction or classification being the most common
application of ML or DL. The most common use cases were: the International
Classification of Diseases, Ninth Revision (ICD-9) classification, clinical
note analysis, and named entity recognition (NER) for clinical descriptions and
research on psychiatric disorders.
  Conclusion: We find that the adopted ML models were not adequately assessed.
In addition, the data imbalance problem is quite important, yet we must find
techniques to address this underlining problem. Future studies should address
key limitations in studies, primarily identifying Lupus Nephritis, Suicide
Attempts, perinatal self-harmed and ICD-9 classification.
