Natural Language Processing for Clinical Entity Extraction from Medical Reports
DOI:
https://doi.org/10.5281/ijurd.v1i2.56Keywords:
Natural Language Processing, Clinical Text, BioBERT, Named Entity Recognition, Medical Reports, Text MiningAbstract
Unstructured medical reports contain valuable clinical information that is difficult to process manually. This study proposes a transformer-based NLP framework using BioBERT for extracting clinical entities. The system identifies diseases, medications, procedures, and anatomical terms with high precision. Evaluation on benchmark datasets shows an F1-score of 0.91. The extracted structured data improves downstream applications such as clinical decision support and automated summarization. The approach significantly reduces manual workload and enhances healthcare analytics capabilities.
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Copyright (c) 2025 Preeti Sandhu, Kiran Das, Nidhi Yadav, Mahesh Choudhary
This work is licensed under a Creative Commons Attribution 4.0 International License.
