A Review of Natural Language Processing for Structured and Unstructured Data in Electronic Health Records
Journal Title: Engineering and Technology Journal - Year 2025, Vol 10, Issue 05
Abstract
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.
Authors and Affiliations
Kazheen Ismael Hasan , Ibrahim Mahmood Ibrahim,
Keywords
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- EP ID EP767905
- DOI 10.47191/etj/v10i05.51
- Views 12
- Downloads 0
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