Medical Named Entity Recognition for Enhanced Electronic Health Record Maintenance

The increasing use of electronic health records (EHRs) has led to a surge in unstructured data, making it challenging to extract valuable insights. This study proposes Natural Language Processing (NLP) based techniques to standardize Electronic Health Record (EHR) data. Conducted in a healthcare setting, the research focuses on transforming unstructured EHR text into structured data using Part-of-Speech tagging and Named Entity Recognition (NER). NER techniques are applied to extract and categorize medical terms, enhancing data accuracy and consistency. The framework’s performance is evaluated using precision and recall rates. Experimental results demonstrate that NER effectively identifies and organizes medical entities, facilitating improved data analysis and decision-making in healthcare. This approach promises to enhance interoperability and the overall utility of EHR systems.

@article{N2025,
  title     = {Medical Named Entity Recognition for Enhanced Electronic Health Record Maintenance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muralikrishna S. N and Raghavendra Ganiga and Raghurama Holla and Ruppikha Sree Shankar},
  doi       = {10.14569/IJACSA.2025.0160383},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160383}
}