Vol. 4 No. 1 (2024): Journal of Machine Learning in Pharmaceutical Research
Articles

Leveraging Deep Learning-Based Natural Language Processing for Enhanced Electronic Health Records: Utilizes deep learning techniques for natural language processing of electronic health records, extracting valuable clinical information for research and healthcare decision-making

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  • Dr. Miguel Hernandez
Dr. Miguel Hernandez
Professor of Bioinformatics, Universidad Autónoma de Barcelona, Spain
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Published 18-05-2024

Keywords

  • Deep learning,
  • natural language processing,
  • electronic health records,
  • clinical information extraction,
  • healthcare decision-making
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. M. Hernandez, “Leveraging Deep Learning-Based Natural Language Processing for Enhanced Electronic Health Records: Utilizes deep learning techniques for natural language processing of electronic health records, extracting valuable clinical information for research and healthcare decision-making”, Journal of Machine Learning in Pharmaceutical Research, vol. 4, no. 1, pp. 72–83, May 2024, Accessed: Sep. 09, 2024. [Online]. Available: https://pharmapub.org/index.php/jmlpr/article/view/15

Abstract

This research paper explores the application of deep learning techniques in natural language processing (NLP) for electronic health records (EHRs). Electronic health records contain a wealth of valuable clinical information, but extracting and analyzing this information manually is time-consuming and error-prone. Deep learning models offer a promising approach to automate and improve the processing of EHRs, enabling more efficient research and healthcare decision-making. This paper provides an overview of deep learning-based NLP techniques for EHRs, discusses their advantages and challenges, and highlights their potential impact on healthcare.

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