A Review of Natural Language Processing for Structured and Unstructured Data in Electronic Health Records

Kazheen Ismael Hasan; Ibrahim Mahmood  Ibrahim

doi:10.47191/etj/v10i05.51

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Kazheen Ismael Hasan Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, KRG - Iraq
Ibrahim Mahmood Ibrahim Computer Networks and Information Security Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, KRG - Iraq

Vol. 10 No. 5 (2025): VOLUME 10 ISSUE 05

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Published May 28, 2025

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This study rigorously analyses the development, methodologies, and efficacy of natural language processing (NLP) applications in healthcare literature from 2020 to 2025. A systematic review of 23 peer-reviewed articles examines how methodological diversity— ranging from rule-based systems to deep learning and large language models—has impacted clinical insights and research outcomes. The analysis indicates a dual trajectory: advanced models like BERT, EXGB, and LSTMs excel with high-performance metrics (F1 ≥ 0.85 in 41% of studies), while traditional rule-based methods endure due to their transparency, specificity, and practicality in low-resource environments. A significant limitation persists: more than 25% of the studies either lack quantitative assessment or depend exclusively on qualitative illustrations, compromising reproducibility and generalizability. Moreover, the limited range of datasets—typically confined to single institutions or specific tasks elicits apprehensions regarding model robustness and external validity. This paper contends that the field is at a pivotal juncture: future advancements require technical expertise and a transition towards interdisciplinary collaboration, standardization in assessment, and ethical awareness in applying NLP in clinical settings. We propose a progressive research agenda harmonizing innovation with accountability and contextual awareness.

Hasan, K. I., & Ibrahim, I. M. (2025). A Review of Natural Language Processing for Structured and Unstructured Data in Electronic Health Records. Engineering And Technology Journal, 10(5), 5227–5239. https://doi.org/10.47191/etj/v10i05.51

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D. H. Maulud et al., “Review on Natural Language Processing Based on Different Techniques,” Asian Journal of Research in Computer Science, pp. 1–17, Jun. 2021, doi: 10.9734/ajrcos/2021/v10i130231.

R. M. Abdullah et al., “Paralinguistic Speech Processing: An Overview,” Asian Journal of Research in Computer Science, pp. 34–46, Jun. 2021, doi: 10.9734/ajrcos/2021/v10i130233.

N. R et al., “Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation,” SLAS Technol, vol. 31, p. 100238, Apr. 2025, doi: 10.1016/J.SLAST.2024.100238.

V. A. Batista and A. G. Evsukoff, “Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review,” Apr. 2023, [Online]. Available: http://arxiv.org/abs/2304.02768

C. H. Chang et al., “New opportunities and challenges for conservation evidence synthesis from advances in natural language processing,” Conservation Biology, vol. 39, no. 2, Apr. 2025, doi: 10.1111/cobi.14464.

J. Ye, L. He, J. Hai, C. Xu, S. Ding, and M. Beestrum, “Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping Review,” Feb. 29, 2024. doi: 10.1101/2024.02.27.24303468.

E. Hossain et al., “Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review,” Jun. 2023, [Online]. Available: http://arxiv.org/abs/2306.12834

M. Omar, S. Nassar, K. SharIf, B. S. Glicksberg, G. N. Nadkarni, and E. Klang, “Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review,” Front Med (Lausanne), vol. 11, Jan. 2025, doi: 10.3389/fmed.2024.1512824.

L. Lilli et al., “A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease,” Stud Health Technol Inform, vol. 316, pp. 909–913, Aug. 2024, doi: 10.3233/SHTI240559.

R. Gatsinga et al., “Current Applications and Developments of Natural Language Processing in Kidney Transplantation: A Scoping Review,” Transplant Proc, vol. 57, no. 4, pp. 558–568, May 2025, doi: 10.1016/J.TRANSPROCEED.2025.02.027.

J. Song, S. Oh, L. Evans, K. H. Bowles, and A. Chair, “Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study,” 2023.

T. A. Koleck, C. Dreisbach, P. E. Bourne, and S. Bakken, “Natural language processing of symptoms documented in free-text narratives of electronic health records: A systematic review,” Feb. 06, 2019, Oxford University Press. doi: 10.1093/jamia/ocy173.

A. Kormilitzin, N. Vaci, Q. Liu, and A. Nevado-Holgado, “Med7: a transferable clinical natural language processing model for electronic health records,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.01271

I. Li et al., “Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review,” Jul. 2021, [Online]. Available: http://arxiv.org/abs/2107.02975

M. Nancy, A. M. Nancy, and R. Maheswari, “A REVIEW ON UNSTRUCTURED DATA IN MEDICAL DATA,” 200AD. [Online]. Available: https://www.researchgate.net/publication/343655656

A. Guazzo, E. Longato, G. P. Fadini, M. L. Morieri, G. Sparacino, and B. Di Camillo, “Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits’ text,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-45115-1.

L. M. Peltonen, H. von Gerich, E. Myllymäki, J. Walsh, and M. Medvecky, “Exploring Delays in Cardiac Care Processes Through Electronic Health Records,” Stud Health Technol Inform, vol. 316, pp. 1866–1870, Aug. 2024, doi: 10.3233/SHTI240795.

N. Cook et al., “Engaging stakeholders with professional or lived experience to improve firearm violence NLP lexicon (Preprint),” Oct. 29, 2024. doi: 10.2196/preprints.68105.

M. Russel Hossain, S. Mahabub, A. Al Masum, and I. Jahan, “Natural Language Processing (NLP) in Analyzing Electronic Health Records for Better Decision Making,” 2024, doi: 10.32996/jcsts.

R. Shankar, A. Bundele, and A. Mukhopadhyay, “Natural language processing of electronic health records for early detection of cognitive decline: a systematic review,” NPJ Digit Med, vol. 8, no. 1, Dec. 2025, doi: 10.1038/s41746-025-01527-z.

J. Shi et al., “Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach,” JMIR Med Inform, vol. 10, no. 8, Aug. 2022, doi: 10.2196/37842.

A. Houston, S. Williams, W. Ricketts, C. Gutteridge, C. Tackaberry, and J. Conibear, “Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study,” BMC Medical Informatics and Decision Making 2024 24:1, vol. 24, no. 1, pp. 1–10, Dec. 2024, doi: 10.1186/S12911-024-02790-Y.

P. Thatoi, A. Shiwlani, H. Qureshi, R. Choudhary, H. A. Qureshi, and S. Kumar, “Natural Language Processing (NLP) in the Extraction of Clinical Information from Electronic Health Records (EHRs) for Cancer Prognosis,” 2023. [Online]. Available: https://www.researchgate.net/publication/383650951

S. Li et al., “Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population,” Comput Biol Med, vol. 189, May 2025, doi: 10.1016/j.compbiomed.2025.109939.

G. A. Ismael et al., “Scheduling Algorithms Implementation for Real Time Operating Systems: A Review,” Asian Journal of Research in Computer Science, pp. 35–51, Sep. 2021, doi: 10.9734/ajrcos/2021/v11i430269.

S. T. Ibrahim, M. Li, J. Patel, and T. R. Katapally, “Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review,” Comput Biol Med, vol. 188, Apr. 2025, doi: 10.1016/j.compbiomed.2025.109859.

I. Mahmood Ibrahim et al., “Task Scheduling Algorithms in Cloud Computing: A Review,” 2021.

D. M. Abdullah et al., “Secure Data Transfer over Internet Using Image Steganography: Review,” Asian Journal of Research in Computer Science, pp. 33–52, Jul. 2021, doi: 10.9734/ajrcos/2021/v10i330243.

Y. Wieland-Jorna, D. Van Kooten, R. A. Verheij, Y. De Man, A. L. Francke, and M. G. Oosterveld-Vlug, “Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review,” Jul. 01, 2024, Oxford University Press. doi: 10.1093/jamiaopen/ooae044.

J. Zeng et al., “Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records,” 2021. [Online]. Available: https://doi.org/10.

Z. Zeng, Y. Deng, X. Li, T. Naumann, and Y. Luo, “Natural Language Processing for EHR-Based Computational Phenotyping,” IEEE/ACM Trans Comput Biol Bioinform, vol. 16, no. 1, pp. 139–153, Jan. 2019, doi: 10.1109/TCBB.2018.2849968.

M. K. S. Uddin, “A REVIEW OF UTILIZING NATURAL LANGUAGE PROCESSING AND AI FOR ADVANCED DATA VISUALIZATION IN REAL-TIME ANALYTICS,” Global Mainstream Journal, vol. 1, no. 4, pp. 34–49, Jul. 2024, doi: 10.62304/ijmisds.v1i04.185.

A. S. Lacey et al., “Obtaining structured clinical data from unstructured data using natural language processing software,” Int J Popul Data Sci, vol. 1, no. 1, Apr. 2017, doi: 10.23889/ijpds.v1i1.381.

K. Du, Y. Zhao, R. Mao, F. Xing, and E. Cambria, “Natural language processing in finance: A survey,” Mar. 01, 2025, Elsevier B.V. doi: 10.1016/j.inffus.2024.102755.

A. Zubiaga, “Natural language processing in the era of large language models,” Front Artif Intell, vol. 6, 2023, doi: 10.3389/frai.2023.1350306.

D. H. Maulud et al., “Review on Natural Language Processing Based on Different Techniques,” Asian Journal of Research in Computer Science, pp. 1–17, Jun. 2021, doi: 10.9734/ajrcos/2021/v10i130231.

S. Golder, D. Xu, K. O’Connor, Y. Wang, M. Batra, and G. G. Hernandez, “Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review,” Apr. 01, 2025, Adis. doi: 10.1007/s40264-024-01505-6.

E. Alskaf et al., “Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records,” Inform Med Unlocked, vol. 44, Jan. 2024, doi: 10.1016/j.imu.2023.101418.

J. Pan et al., “Integrating large language models with human expertise for disease detection in electronic health records,” Comput Biol Med, vol. 191, Jun. 2025, doi: 10.1016/j.compbiomed.2025.110161.

A. Kormilitzin, N. Vaci, Q. Liu, and A. Nevado-Holgado, “Med7: a transferable clinical natural language processing model for electronic health records,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.01271

S. Rajendran and U. Topaloglu, “Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.”

M. D. Solomon, G. Tabada, A. Allen, S. H. Sung, and A. S. Go, “Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records,” Cardiovasc Digit Health J, vol. 2, no. 3, pp. 156–163, Jun. 2021, doi: 10.1016/j.cvdhj.2021.03.003.

T. Lo Barco, M. Kuchenbuch, N. Garcelon, A. Neuraz, and R. Nabbout, “Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome,” Orphanet J Rare Dis, vol. 16, no. 1, Dec. 2021, doi: 10.1186/s13023-021-01936-9.

F. R. Tsui et al., “Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts,” JAMIA Open, vol. 4, no. 1, Jan. 2021, doi: 10.1093/jamiaopen/ooab011.

E. Hatef et al., “Development and assessment of a natural language processing model to identify residential instability in electronic health records’ unstructured data: A comparison of 3 integrated healthcare delivery systems,” JAMIA Open, vol. 5, no. 1, Apr. 2022, doi: 10.1093/jamiaopen/ooac006.

S. Han et al., “Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing,” Mar. 01, 2022, Academic Press Inc. doi: 10.1016/j.jbi.2021.103984.

D. H. P. Benício, J. C. Xavier Junior, K. R. S. de Paiva, and J. D. de A. S. Camargo, “Applying Text Mining and Natural Language Processing to Electronic Medical Records for extracting and transforming texts into structured data,” Research, Society and Development, vol. 11, no. 6, p. e37711629184, Apr. 2022, doi: 10.33448/rsd-v11i6.29184.

K. Lybarger et al., “Leveraging natural language processing to augment structured social determinants of health data in the electronic health record,” J Am Med Inform Assoc, vol. 30, no. 8, pp. 1389–1397, Aug. 2023, doi: 10.1093/jamia/ocad073.

S. Liu et al., “Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record,” Int J Med Inform, vol. 177, Sep. 2023, doi: 10.1016/j.ijmedinf.2023.105136.

H. Barman et al., “Retrospective study of propionic acidemia using natural language processing in Mayo Clinic electronic health record data,” Mol Genet Metab, vol. 140, no. 3, Nov. 2023, doi: 10.1016/j.ymgme.2023.107695.

J. Lin et al., “Identification of Health Conditions in Unstructured Health Records with Deep Learning-Based Natural Language Processing,” Oct. 10, 2024. doi: 10.1101/2024.10.08.24315141.

A. A. More, “Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes,” Journal of Contemporary Medical Practice, vol. 6, no. 8, pp. 327–330, Aug. 2024, doi: 10.53469/jcmp.2024.06(08).67.

D. Badalotti, A. Agrawal, U. Pensato, G. Angelotti, and S. Marcheselli, “Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center,” Int J Med Inform, vol. 192, Dec. 2024, doi: 10.1016/j.ijmedinf.2024.105626.

E. A. Martin, A. G. D’Souza, V. Saini, K. Tang, H. Quan, and C. A. Eastwood, “Extracting social determinants of health from inpatient electronic medical records using natural language processing,” Journal of epidemiology and population health, vol. 72, no. 6, Dec. 2024, doi: 10.1016/j.jeph.2024.202791.

J. C. Ruckdeschel et al., “Unstructured Data Are Superior to Structured Data for Eliciting Quantitative Smoking History From the Electronic Health Record,” 2023, doi: 10.1200/CCI.22.

B. Clay, H. I. Bergman, S. Salim, G. Pergola, J. Shalhoub, and A. H. Davies, “Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies,” Apr. 01, 2025, Elsevier Ltd. doi: 10.1016/j.compbiomed.2025.109808.

X. Chen, W. Tian, and H. Fang, “Bibliometric analysis of natural language processing using CiteSpace and VOSviewer,” Natural Language Processing Journal, vol. 10, p. 100123, Mar. 2025, doi: 10.1016/j.nlp.2024.100123.

T. Takeuchi et al., “A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records,” BMC Med Inform Decis Mak, vol. 25, no. 1, p. 85, Dec. 2025, doi: 10.1186/s12911-025-02928-6.

A. B. Alnor, R. B. Lynggaard, M. S. Laursen, and P. J. Vinholt, “Natural language processing for identifying major bleeding risk in hospitalised medical patients,” Comput Biol Med, vol. 190, May 2025, doi: 10.1016/j.compbiomed.2025.110093.

C. Soto Jacome et al., “Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records,” Mayo Clinic Proceedings: Digital Health, vol. 2, no. 1, pp. 67–74, Mar. 2024, doi: 10.1016/j.mcpdig.2024.01.001.

B. G. Patra et al., “Extracting social determinants of health from electronic health records using natural language processing: A systematic review,” Dec. 01, 2021, Oxford University Press. doi: 10.1093/jamia/ocab170.

M. Russel Hossain, S. Mahabub, A. Al Masum, and I. Jahan, “Natural Language Processing (NLP) in Analyzing Electronic Health Records for Better Decision Making,” 2024, doi: 10.32996/jcsts.

A Review of Natural Language Processing for Structured and Unstructured Data in Electronic Health Records

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