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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Summary
This summary is machine-generated.

This study demonstrates how natural language processing (NLP) can extract valuable information from unstructured clinical notes. A curated ontology enables the conversion of clinical narratives into structured data for research.

Keywords:
Biomedical OntologiesData WarehousingNatural Language Processing

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Area of Science:

  • Medical Informatics
  • Natural Language Processing

Background:

  • Unstructured clinical notes are rich in information but difficult to access.
  • Existing methods for data extraction are often complex and resource-intensive.

Purpose of the Study:

  • To investigate the feasibility of using NLP to harvest information from unstructured clinical notes.
  • To develop a streamlined process for converting clinical narratives into structured data.

Main Methods:

  • An NLP-based approach was employed.
  • A manually curated ontology was utilized to guide the information extraction process.
  • The method was tested on pathology reports from Papa Giovanni XXIII hospital.

Main Results:

  • The NLP approach successfully processed unstructured clinical notes.
  • A structured data warehouse (i2b2) was populated using the extracted information.
  • The methodology proved effective for pathology reports dating back to 2008.

Conclusions:

  • NLP, supported by a curated ontology, offers a viable method for extracting structured data from unstructured clinical text.
  • This approach facilitates the utilization of historical clinical data for research and analysis.