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Cited by 407Machine Learning in HealthcareBiomedical Text Mining and OntologiesTopic Modeling

The DOI matches its Crossref record; 152 of the 161 references it cites were found in a registry or an index.

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review

Hossain, Elias, Rana, Rajib, Higgins, Niall, Soar, Jeffrey, Barua, Prabal Datta, Pisani, Anthony R., Turner, Kathryn·2023·Computers in Biology and Medicine
DOI 10.1016/j.compbiomed.2023.106649
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Abstract

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.

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Research works printed but not linked: 5

  1. Juhn, 2019, Natural language processing to advance EHR-based clinical research in Allergy, Asthma, and Immunology, J. Allergy Clin. Immunol., 145
  2. Kormilitzin, 2021, Med7: a transferable clinical natural language processing model for electronic health records, Artif. Intell. Med., 118, 10.1016/j.artmed.2021.102086 · DOI
  3. Gligic, 2020, Named entity recognition in electronic health records using transfer learning bootstrapped neural networks, Neural Netw., 121, 132, 10.1016/j.neunet.2019.08.032 · DOI
  4. Shen, 2021, Analysis of electronic health records based on deep learning with natural language processing, Arab. J. Sci. Eng., 1
  5. kormilitzin, 2021, Med7: a transferable clinical natural language processing model for electronic health records, Artif. Intell. Med., 118, 10.1016/j.artmed.2021.102086 · DOI

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Metadata

Authors
Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K.
Affiliations
  • North South University (BD)
  • University of Southern Queensland (AU)
  • Queensland University of Technology (AU)
  • University of Rochester Medicine (US)
Corresponding author
Hossain, Elias
ORCID
Venue
Computers in Biology and Medicine
Publisher
Elsevier BV
Published
2023-03
Volume
155
Pages
106649
ISSN
0010-4825 (ISSN-L)
DOI
10.1016/j.compbiomed.2023.106649
OpenAlex
W4319985906
Cited by
407
References
161
Citations by year
2026: 103 · 2025: 177 · 2024: 105 · 2023: 22
FWCI
8.55
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Computers in Biology and Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institutes of Health
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
natural language processing, electronic health records, healthcare decision-making, named entity recognition, clinical information extraction, ICD-9, transfer learning, class imbalance, clinical text
MeSH terms
Machine Learning, Delivery of Health Care, Humans, Natural Language Processing, Information Storage and Retrieval, Electronic Health Records

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