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Research Article

Natural Language Processing (NLP) in Analyzing Electronic Health Records for Better Decision Making

Authors

  • Md Russel Hossain MS in Information Technology, Washington University of Science and Technology, USA
  • Shohoni Mahabub MS in Information Technology, Washington University of Science and Technology, USA
  • Abdullah Al Masum MS in Information Technology, Westcliff University, USA
  • Israt Jahan MS in Information Technology, Washington University of Science and Technology, USA

Abstract

Natural Language Processing (NLP) is transforming healthcare decision-making by extracting valuable insights from Electronic Health Records (EHR). This paper explores the integration of NLP with EHR systems, focusing on its potential to enhance clinical workflows, patient outcomes, and the accuracy of healthcare decision-making. Using advanced NLP techniques, such as BERT and spaCy, the study analyzes both structured and unstructured EHR data to uncover patterns in diagnosis, treatment recommendations, and patient outcomes. The study compares NLP-based analysis with traditional data analysis methods, demonstrating its effectiveness in improving clinical decision support systems. Despite the promising potential, challenges such as data quality, model interpretability, and seamless integration with existing healthcare systems are highlighted. The paper concludes by emphasizing the need for continued advancements in NLP models and real-time data processing, suggesting future research directions to optimize the implementation of NLP in healthcare settings.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

6 (5)

Pages

216-228

Published

2024-12-22

How to Cite

Md Russel Hossain, Shohoni Mahabub, Abdullah Al Masum, & Israt Jahan. (2024). Natural Language Processing (NLP) in Analyzing Electronic Health Records for Better Decision Making. Journal of Computer Science and Technology Studies, 6(5), 216-228. https://doi.org/10.32996/jcsts.2024.6.5.18

References

[1] Boyd, A. D., Li, J., Burton, M. D., Jonen, M., Gardeux, V., & Achour, I. (2017). Big data in healthcare: Data management and data analytics. Journal of Bioinformatics, 14(3), 45-56. https://doi.org/10.1016/j.jbio.2017.03.001

[2] Boyd, A. D., Liu, H., & Yu, H. (2017). Applications of topic modeling in clinical research. Journal of Biomedical Informatics, 75, 120-128. https://doi.org/10.1016/j.jbi.2017.09.014

[3] Chowdhury, R. H. (2024). AI-driven business analytics for operational efficiency. World Journal of Advanced Engineering Technology and Sciences, 12(2), 535–543

[4] Chowdhury, R. H. (2024). Intelligent systems for healthcare diagnostics and treatment. World Journal of Advanced Research and Reviews (WJARR), 23(1), 007–015. World Journal Series. eISSN: 2581-9615.

[5] Chowdhury, R. H. (2024). The evolution of business operations: Unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain. World Journal of Advanced Research and Reviews (WJARR), 22(3), 2135–2147

[6] Chowdhury, R. H. (2024). Big data analytics in the field of multifaceted analyses: A study on health care management. World Journal of Advanced Research and Reviews, 22(3), 2165–2172.

[7] Chowdhury, R. H. (2024). Harnessing machine learning in business analytics for enhanced decision-making. World Journal of Advanced Engineering Technology and Sciences, 12(2), 674–683.

[8] Chowdhury, R. H. (2024). The evolution of business operations: Unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain. World Journal of Advanced Research and Reviews (WJARR), 22(3), 2135–2147.

[9] Cohen, A. M., Gasser, J., Hersh, W. R., & Friedman, C. (2014). A survey of current work in biomedical text mining. Briefings in Bioinformatics, 15(1), 32-44. https://doi.org/10.1093/bib/bbt007

[10] Cohen, K. B., Demner-Fushman, D., Chapman, W. W., & Peterson, K. (2014). Natural language processing in clinical and translational research: A systematic review. Journal of the American Medical Informatics Association, 21(6), 1120-1130. https://doi.org/10.1136/amiajnl-2013-002707

[11] Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.

[12] Dernoncourt, F., Lee, J. Y., Uzuner, Ö., & Szolovits, P. (2017). De-identification of patient notes with recurrent neural networks. Journal of the American Medical Informatics Association, 24(3), 596-606.

[13] Huang, K., Altosaar, J., & Ranganath, R. (2019). ClinicalBERT: Modeling clinical notes and predicting hospital readmission. Journal of Biomedical Informatics, 97, 103236. https://doi.org/10.1016/j.jbi.2019.103236

[14] Huang, Y., McCullough, M. B., Xu, H., & Liu, C. (2019). Integration of NLP and wearable device data in personalized healthcare: Emerging trends and future directions. Journal of the American Medical Informatics Association, 26(6), 561-570. https://doi.org/10.1093/jamia/ocz012

[15] Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395-405.

[16] Juhn, Y. J., & Liu, H. (2020). Toward integrating EHR data and NLP to improve healthcare outcomes: A practical review of recent advancements. BMC Medical Informatics and Decision Making, 20(1), 282. https://doi.org/10.1186/s12911-020-01284-7

[17] Juhn, Y. J., & Liu, H. (2020). Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. Journal of Allergy and Clinical Immunology, 145(2), 463-469. https://doi.org/10.1016/j.jaci.2019.12.905

[18] Khurana, M., et al. (2021). Strategies for effective healthcare data management. Journal of Medical Informatics, 28(1), 12-23.

[19] Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C., & Hurdle, J. F. (2008). Extracting information from textual documents in the electronic health record: A review of recent research. Yearbook of Medical Informatics, 2008, 128-144.

[20] Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to healthcare. JAMA, 309(13), 1351-1352. https://doi.org/10.1001/jama.2013.393

[21] Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259

[22] Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 378(23), 2275-2284. https://doi.org/10.1056/NEJMra1814259

[23] Savova, G. K., et al. (2010). Mayo Clinic Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation, and applications. Journal of the American Medical Informatics Association, 17(5), 507-513.

[24] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. Journal of Biomedical Informatics, 83, 275-293.

[25] Shivade, C., Raghavan, P., Fosler-Lussier, E., & Embi, P. J. (2014). A review of approaches to identifying patient phenotype cohorts using electronic health records. Journal of the American Medical Informatics Association, 21(2), 221-230.

[26] Weng, C., et al. (2017). Automated clinical trial eligibility prescreening: Increasing the efficiency of patient recruitment. Journal of the American Medical Informatics Association, 24(1), 163-171.

[27] Zeng, Z., Deng, Y., Li, X., Naumann, T., & Luo, Y. (2018). Natural language processing for EHR-based computational phenotyping. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(1), 139-153.

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Keywords:

Natural Language Processing (NLP), Electronic Health Records (EHR), Healthcare Decision-Making, Clinical Decision Support, Data Analysis, BERT, Structured Data, Unstructured Data, Machine Learning, Healthcare Systems Integration.