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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]
Citations
Cites
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312
Big Data and Machine Learning in Health Care
Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
Gray literature: An important resource in systematic reviews
Paez, A. (2017) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12266 · Cited by 1,459
2013 IEEE International Conference on Big Data
(2013) · doi:10.1109/bigdata21311.2013 · Cited by 1,406
Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationPreprint
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06560 · Cited by 1,368
(2015) · neural information processing systems · Cited by 1,305
Research works printed but not linked: 5
- Juhn, 2019, Natural language processing to advance EHR-based clinical research in Allergy, Asthma, and Immunology, J. Allergy Clin. Immunol., 145
- 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
- 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
- Shen, 2021, Analysis of electronic health records based on deep learning with natural language processing, Arab. J. Sci. Eng., 1
- 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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Chen, Y., Lehmann, C. U., & Malin, B. (2024) · Journal of Medical Internet Research · doi:10.2196/60258 · Cited by 75
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InfluentialSandu, A. S., Chifiriuc, M. C., Vrâncianu, C. O., Cristian, R.-E., ALISTAR, C. F., Constantin, M., Păun, M., Alistar, A., Popa, G. L., Popa, M. I., et al. (2025) · Infectious Diseases and Therapy · doi:10.1007/s40121-025-01143-0 · Cited by 76
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A survey on cutting-edge relation extraction techniques based on language models
Diaz-Garcia, J. A., & Amador, J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11280-0 · Cited by 32
Vosoughi, P., Naghib, S. M., & Takdehghan, G. (2025) · Sensors and Actuators Reports · doi:10.1016/j.snr.2025.100385 · Cited by 19
Guérin, J., Nahid, A., Tassy, L., Deloger, M., Bocquet, F., Thézenas, S., Désandes, E., Deley, M. L., Durando, X., Jaffré, A., et al. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21020189 · Cited by 16
“Moreover, the systematic review of clinical text data in machine learning highlights the capability of NLP to unlock valuable clinical narratives for extensive analysis [27], while other studies emphasise the suitability of various deep learning algorithms for EHR applications [28].”
BackgroundPawuś, D., Porażko, T., & Paszkiel, S. (2024) · IEEE Access · doi:10.1109/access.2024.3413595 · Cited by 21
“Article [52] investigates the challenges and opportunities in applying NLP to extract clinical insights from Electronic Health Records (EHRs).”
BackgroundXiao, N., Huang, X., Wu, Y., Li, B., Zang, W., Shinwari, K., Тузанкина, И. А., Черешнев, В. А., & Liu, G. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1523902 · Cited by 20
Generative artificial intelligence for automated data extraction from unstructured medical text
Dao, N., Quesada, L., Hassan, S. M., Campo, M. I., Johnson, S. W., Ghose, S., Estépar, R. S. J., Waxman, A. B., Washko, G. R., & Rahaghi, F. N. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf097 · Cited by 17
How large language models can augment perioperative medicine: a daring discourse
Gabriel, R. A., Mariano, E. R., McAuley, J., & Wu, C. L. (2023) · Regional Anesthesia & Pain Medicine · doi:10.1136/rapm-2023-104637 · Cited by 15
Xiaoqing: A Q&A model for glaucoma based on LLMs
Xue, X., Zhang, D., Sun, C., Shi, Y., Wang, R., Tan, T., Gao, P., Fan, S., Zhai, G., Hu, M., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108399 · Cited by 22
The Impact of ChatGPT for Enhancing Knowledge Management in University Libraries
Rahman, M. H., & Islam, M. N. (2024) · Journal of Web Librarianship · doi:10.1080/19322909.2024.2391907 · Cited by 19
“The integration of advanced technologies, particularly natural language processing (NLP) models, into the realm of knowledge management, has seen increasing scholarly interest in recent years (Arnarsson et al., 2021; Hossain et al., 2023; Kang et al., 2020; Petroșanu et al., 2023).”
Sajjadi, S. M., Mohebbi, A., Ehsani, A., Marashi, A. H., Azhdarimoghaddam, A., Karami, S., Karimi, M. A., Sadeghi, M., Firoozi, K., Zamani, A. M., et al. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-04810-5 · Cited by 14
Can ChatGPT outperform a neurosurgical trainee? a prospective comparative study.
Williams, S. C., Starup-Hansen, J., Funnell, J. P., Hanrahan, J. G., Valetopoulou, A., Singh, N., Sinha, S., Muirhead, W. R., & Marcus, H. J. (2025) · British Journal of Neurosurgery · doi:10.1080/02688697.2024.2308222 · Cited by 13
“24 29 Further, the use of LLMs in medical education and interview preparation is likely to expand.”
Honap, S., Danese, S., & Peyrin‐Biroulet, L. (2024) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izae131 · Cited by 13
“28,29 Examples of NLP in gastroenterology include a model that was able to diagnose eosino-philic esophagitis using retrospectively extracted clinical and esophagogastroduodenoscopy data, and a model that was able to identify IBD patients on thiopurines with algorithm-predicted objective remission.”
Shaheen, A. R., Afflitto, G. G., & Swaminathan, S. S. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100602 · Cited by 13
“12 Fink et al showed the superiority of ChatGPT and GPT-4 models in data mining and labeling tasks compared with manual annotation methods of extracting oncologic phenotypes from free-text radiographic reports for lung cancer.”
Karpagavadivu, K., Sakthivel, S., Ramya, M., Kaliappan, S., Maranan, R., & Nitheesh, S. (2024) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig64922.2024.10911601 · Cited by 13
“Classifying such tweets and other posts related to health can help researchers analyze the attitude of the population or identify fake news regarding health issues and other trends in such discussions [3].”
Abdalla, M., Saad, M., Abazia, D. T., Advani, A., Alhammad, A., Bernknopf, A. C., Bitra, V. R., Blommel, M. L., Conn, K. M., Goldwire, M. A., et al. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70131 · Cited by 14
“AI and chatbots can assist clinicians with answering routine medication questions, refill reminders, and adherence and patient monitoring [4, 44–47].”
InfluentialThe transformative role of Artificial Intelligence in genomics: Opportunities and challenges
Dara, M., Dianatpour, M., Azarpira, N., & Tanideh, N. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102314 · Cited by 12
de Oliveira, R. S., & Nascimento, E. G. S. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104477 · Cited by 12
Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes
Morales-Sánchez, R., Montalvo, S., Riaño, A., Martínez, R., & Velasco, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108830 · Cited by 11
Computational modeling for medical data: From data collection to knowledge discovery
Yin, Y., Xu, S., Hong, Y., Cai, Y., Tang, W., Wang, J., Shen, B., Zong, H., & Yu, G. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100079 · Cited by 11
Bibliometric analysis of natural language processing using CiteSpace and VOSviewer
Chen, X., Tian, W., & Fang, H. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100123 · Cited by 22
Guan, H., Novoa-Laurentiev, J., & Zhou, L. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104830 · Cited by 12
Vithanage, D., Yu, P., Xie, Q., Xu, H., Wang, L., & Deng, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111013 · Cited by 10
Salehin, I., Sajib, M. T. A., Badhon, N. H., Rifat, M. S. H., Amin, N., & Moon, N. N. (2025) · Engineering Reports · doi:10.1002/eng2.70365 · Cited by 9
“NLP is essential to the healthcare industry because it can convert vast amounts of unstructured data in electronic health records into forms that are helpful for diagnosis, treatment planning, and decision-making [82].”
A two-stream deep model for automated ICD-9 code prediction in an intensive care unit
Ayden, M. A., Yüksel, M. E., & Yüksel, S. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25960 · Cited by 10
“As a result, the advancements in deep learning techniques within the field of natural language processing have led to numerous studies in this domain [12,13].”
BackgroundSingh, A. K., Sharma, K. K., Bajpai, M. K., & Cabezuelo, A. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112374 · Cited by 11
Rawas, S., Tafran, C., AlSaeed, D., & Al-Ghreimil, N. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.055307 · Cited by 15
“A systematic review conducted by Hossain et al. [27] comprehensively examines various machine learning (ML) and deep learning (DL) techniques employed in NLP for EHRs, highlighting their effectiveness in extracting clinical insights from unstructured data.”
Xie, F., Chang, J., Luong, T. Q., Wu, B. U., Lustigova, E., Shrader, E. E., & Chen, W. (2023) · JMIR AI · doi:10.2196/51240 · Cited by 8
“They have been widely used for analyzing information from unstructured notes in the health care domain [18,19,37].”
MethodLakhdari, W., Bouhenna, M. M., Nacer, S. N., Dehliz, A., Benyahia, I., Bendıf, H., & Garzoli, S. (2024) · Biochemical Systematics and Ecology · doi:10.1016/j.bse.2024.104881 · Cited by 9
Roosan, D., Chok, J. I., Li, Y., & Khou, T. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698600 · Cited by 9
“This gap in research underscores the need for comprehensive studies that not only harness the capabilities of Transformer-based NLP models for SDOH extraction but also evaluates their effectiveness in real-world healthcare settings [13].”
BackgroundContrasting rule and machine learning based digital self triage systems in the USA
Naved, B. A., & Luo, Y. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01367-3 · Cited by 9
“If provided with feedback on the correctness of an individual patient ’ s triage, machine learning-based systems may enable more automated improvement over time 96,97 .”
Choi, A., Kim, C. H., Ryoo, J., Jeon, J., Cho, S., Lee, D., Kim, J., Lee, C., & Bae, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-87161-x · Cited by 10
“Despite their widespread use and clear benefits, the complete utilization of DL-based NLP method in clinical settings is limited by the lack of annotated data and automated tools that are essential for effectively extracting clinical insights 15 .”
From Promise to Practice: Harnessing AI’s Power to Transform Medicine
Genovese, A., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Prabha, S., Trabilsy, M., & Forte, A. J. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14041225 · Cited by 10
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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