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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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“29 Additionally, recurrent neural networks (RNNs) and their advanced variants (e.g., LSTM and Transformer) have demonstrated exceptional capabilities in analyzing physiological signals such as ECG and electroencephalo-grams (EEG), effectively identifying complex temporal patterns and supporting…”
Moz, S. H., Hosen, M. A., Santo, M. N. S., Kabir, S. S., Adnan, M. N., & Galib, S. M. (2023) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2023.4.02 · Cited by 3
Tripathy, G., & Sharaff, A. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02202-7 · Cited by 4
Venosa, M., Calvisi, V., Iademarco, G., Romanini, E., Ciminello, E., Cerciello, S., & Logroscino, G. (2024) · Prosthesis · doi:10.3390/prosthesis6040066 · Cited by 3
“…models like ChatGPT have emerged as potential gateways to disseminate knowledge and address inquiries related to complex medical procedures [8,9]. digital age, AI language models like ChatGPT have emerged as potential gateways to disseminate knowledge and address inquiries related to…”
BackgroundMedical Document Embedding Enhancement with Heterogeneous Mixture-of-Experts
Liu, X., Zhu, Y., Pang, T., Xue, K., Zhang, X., & Fan, C. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822374 · Cited by 4
“In the medical domain, text embedding models are used for tasks such as question-answering [2], [34], literature retrieval [33], [34], EHR analysis [7], [19], and clinical decision-making [2], [10].”
Lu, J., Liang, W., Cui, L., Mou, S., Pei, X., Shen, X., Shen, Z., & Shen, P. (2025) · Neuropsychobiology · doi:10.1159/000543646 · Cited by 5
“Various types of data sources, such as neuroimaging, voice and language patterns, and mobile phone data, among others, have commonly been employed for model building [49, 50].”
Heybe, M. A., Gibson, L. L., Price, A. C., Cardinal, R. N., O'Brien, J. T., Stewart, R. J., & Mueller, C. (2025) · npj Aging · doi:10.1038/s41514-025-00252-x · Cited by 3
“Natural language processing (NLP) provides a powerful tool for mining and analysing unstructured data within EHRs, enabling the extraction of clinically relevantinformationsuchassymptompatternsand disease markers 8,9 .”
RAGnosis: Retrieval-Augmented Generation for Enhanced Medical Decision MakingPreprint
Rouhollahi, A., Homaei, A., Sahu, A., Harari, R. E., & Nezami, F. R. (2025) · medRxiv · doi:10.1101/2025.06.11.25329438 · Cited by 3
“Despite widespread adoption of EHRs, critical information often remains underutilized due to the limitations of conventional data processing techniques and the lack of semantic interoperability across systems [1,2].”
Marletta, D. A., Nanni, M., Giuca, G., Sanzarello, I., Zampogna, B., & Leonetti, D. (2024) · Applied Sciences · doi:10.3390/app142411652 · Cited by 3
López-Miranda, E., Tolosa, P., Perelló-Martorell, M. A., Sánchez‐Lorenzo, L., Hinojo, C., Servitja, S., Penabad, S. R., Olier, C., Guerrero, J. A., García‐Vicente, S., et al. (2025) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100107 · Cited by 2
“11,12 NLP could be helpful in extracting real-world data (RWD) for clinical research and help data scientists and physicians in the decision-making process.”
Choi, Y. C., Poppe, K., Selak, V., Moffitt, A., Chung, C. Y. S., Ullmer, J., & Wells, S. (2025) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101393 · Cited by 2
“In line with recent work by Hossain et al , 19 which highlighted challenges with misclassification due to ambiguous phrasing and negation in free-text entries, our study reinforces the notion that while sophisticated algorithms can enhance accuracy, simpler methods can also be effective when…”
Wu, D., Liu, N., Ma, R., & Wu, P. (2025) · Journal of Medical Internet Research · doi:10.2196/71970 · Cited by 2
“By using NLP techniques, clinicians and researchers can extract and standardize data, enabling them to navigate through large volumes of electronic medical records (EMRs) more effectively [49,50].”
Bosniak classification of renal cysts using large language models: a comparative study
Hacıbey, İ., & Kaba, E. (2025) · Die Radiologie · doi:10.1007/s00117-025-01499-x · Cited by 2
Feng, E., Jayasuriya, N., & Kirby, D. (2025) · SurgiColl · doi:10.58616/001c.137606 · Cited by 2
Behzad, S., Boucher, J. O., Alavijeh, N. S., Ravi, K., & Gholamrezanezhad, A. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.07.057 · Cited by 2
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“LC is the guarantor.”
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“Among AI applications, natural language processing (NLP) and machine learning (ML) are especially promising for extracting critical information from electronic health records (EHRs) [13, 14].”
Sourkatti, H., Aşuroğlu, T., Itkonen, M., Alahäivälä, A.-L. I., Tolppanen, A.-M., Ihalainen, J. A., & Forsberg, M. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101703 · Cited by 2
Henna, F., Batool, S. K., Siddiqui, H. T., Riaz, A., Javed, A., Kamran, A., Faisal, F., Shadab, H. A., Qamar, A., Mehboob, J., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001071 · Cited by 4
Transition to Artificial Intelligence in Imaging and Laboratory Diagnostics in Rheumatology
Dimitrov, S., Bogdanova, S., Apostolova, Z., Kasapska, B., Kabakchieva, P., & Georgiev, T. (2025) · Applied Sciences · doi:10.3390/app152111666 · Cited by 4
Contrastive text embeddings with effective sample mining for enhanced disease diagnosis
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Bhattacharya, M., Chatterjee, S., Das, A., Lee, S., & Chakraborty, C. (2025) · Vaccine · doi:10.1016/j.vaccine.2025.128051 · Cited by 2
Artificial intelligence in radiobiology: Bridging mechanisms and data analysis
Wang, Y., Li, W., Xu, N., Xie, D., Huang, X., & Zhou, P. (2025) · Radiation Medicine and Protection · doi:10.1016/j.radmp.2025.12.005 · Cited by 2
Srinivasan, S., Rizy, C., Mahbub, M., Bolme, D. S., Peluso, A., Trafton, J. A., & Danciu, I. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106247 · Cited by 2
Huang, X., Goh, H. H., He, T., Zhang, D.-D., Dai, W., Kurniawan, T. A., Goh, K. C., Wong, H. Y., Islam, M. T., & Liang, X. (2026) · Journal of Chinese Integrative Medicine · doi:10.1016/j.joim.2026.02.004 · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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