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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Graph Clustering System for Text-Based Records in a Clinical Pathway.

Takanori Yamashita1, Naoya Onimura2, Hidehisa Soejima3

  • 1Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.

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This study introduces a novel clustering method to analyze unstructured clinical text data, creating "sentence graphs" to represent patient conditions and improve medical treatment standardization.

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Critical PathwaysData MiningMathematical Computing

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Data Science

Background:

  • Digitization of medical records generates vast amounts of structured and unstructured data.
  • Analysis of unstructured text data in electronic medical records (EMRs) holds potential for improving healthcare processes.
  • Existing research on text-based medical record processing has limited direct impact on clinical practice.

Purpose of the Study:

  • To present a clustering approach for identifying typical patient conditions from clinical pathway text-based EMRs.
  • To enhance the analysis of unstructured medical data for practical medical applications.
  • To contribute to the standardization of text-based medical records.

Main Methods:

  • A clustering approach is employed to classify sentences from text-based EMRs.
  • The Louvain method is used for classifying feature words within clusters.
  • Sentences within each cluster are merged to form a "sentence graph" representing medical processes.

Main Results:

  • Analysis of real-world text-based EMRs demonstrates that sentence graphs effectively represent medical treatments and patient conditions.
  • The proposed method shows potential for standardizing text-based medical records.
  • The approach aids in recognizing key medical processes within clinical pathways.

Conclusions:

  • Sentence graphs derived from clustering unstructured EMR text can visualize patient conditions and treatment pathways.
  • This method offers a pathway to standardize medical records and improve recognition of critical medical processes.
  • The findings suggest a significant improvement in medical treatment through better data analysis.