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Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review
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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“To address this, natural language processing (NLP) techniques such as named entity recognition (NER), text classification, and relation extraction are used [38].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [38]
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Mahamkali, S., Dandola, R., Salkunti, R. R., & Balakrishna, P. (2026) · IEEE Global Symposium on Emerging and Communication Technologies (GSEACT) · doi:10.1109/gseact68539.2026.11620514
“Large-scale reviews often report severe class imbalance, limited dataset sizes, and dependence on single-institution corpora, restricting reproducibility and external real-world validity [1], [2].”
Explainable AI for Clinical Oncology Decisions
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Industry Practitioners Developing or Implementing AI Platforms for Healthcare and Life Sciences
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“Advances in natural language processing, a subset of AI, allow automated insights into EHR data, including note classification, entity recognition, and text summarization [10].”
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Graph-based comorbidity analysis: a systematic literature review and taxonomy
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Natural Language Processing-Driven Clinical Decision Support using Electronic Health Records
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SQC-Guard: A Statistical Quality Control Based Monitoring Framework for NLP Pipelines
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“However, these comprehensive data are often highly unstructured and heterogeneous in format, posing significant challenges for traditional RNN-and CNN-based models (Hossain et al., 2023; Kim M. K. et al., 2024).”
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)
- 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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