A Novel Representation of Knowledge Discovery in Clinical Text Data using Knowledge Graphs

Authors

  • Naveen S. Pagad, Pradeep N.

Keywords:

Clinical Bert, NetworkX and NLP.

Abstract

In the ever-evolving landscape of healthcare, the utilization of natural language processing (NLP) techniques has emerged as a pivotal tool for uncovering valuable insights from clinical text data. In this paper, we present a novel approach that leverages the power of Advanced Healthcare NLP Model, an advanced NLP model fine-tuned for medical text, to enable end-to-end intelligent data extraction and structured medical data visualization in healthcare. Our methodology encompasses a comprehensive process that begins with the collection and pre-processing of clinical text data, followed by fine-tuning of the Advanced Healthcare NLP Model model to extract entities and relationships. Subsequently, we employ NetworkX, a Python library for graph analysis, in conjunction with Matplotlib for visualization, to construct and analyze a Knowledge Graph representation of the extracted information. Through this integrated approach, we demonstrate the capability to uncover intricate medical insights, identify meaningful relationships between medical concepts, and represent them in a structured and interpretable manner. Our findings showcase the potential of leveraging state-of-the-art NLP techniques in conjunction with graph-based representations to advance healthcare research, clinical decision-making and patient outcomes.

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Published

12.06.2024

How to Cite

Naveen S. Pagad. (2024). A Novel Representation of Knowledge Discovery in Clinical Text Data using Knowledge Graphs. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5003 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7258

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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