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Automated Information Extraction on Treatment and Prognosis for Non-Small Cell Lung Cancer Radiotherapy Patients:

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Summary

The IDEAL-X platform efficiently extracts crucial treatment and prognosis data from oncology patient records, improving large-scale clinical studies for non-small cell lung cancer (NSCLC). This tool enhances data accuracy and accessibility for radiation therapy research.

Keywords:
chemoradiation treatmentinformation extractioninformation storage and retrievalnatural language processingnon–small cell lungoncologyprognosis

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Area of Science:

  • Oncology
  • Medical Informatics
  • Clinical Data Management

Background:

  • Oncology studies require extracting survival, toxicity, and complication data from patient records.
  • Manual data extraction from narrative or tabular formats is time-consuming and labor-intensive.
  • Heterogeneous vocabularies in medical records hinder efficient data retrieval.

Purpose of the Study:

  • To adapt the Information and Data Extraction using Adaptive Learning (IDEAL-X) platform.
  • To extract treatment and prognosis data for non-small cell lung cancer (NSCLC) patients.
  • To facilitate data navigation and analysis in clinical studies.

Main Methods:

  • Transformed patient documents into normalized, structured forms using IDEAL-X.
  • Applied adaptive learning and customized controlled toxicity vocabularies.
  • Extracted and categorized treatment and prognosis data into structured output.

Main Results:

  • Extracted data from 261 documents for 50 NSCLC patients.
  • Achieved overall precision >93% and recall >83% for treatment and prognosis data.
  • Attained 95.7% precision and 94.5% recall for toxicity information extraction.

Conclusions:

  • IDEAL-X accurately and effectively extracts study data for NSCLC chemoradiation patients.
  • The system is suitable for large-scale radiotherapy clinical data studies.
  • Enhances efficiency in analyzing patient outcomes and treatment efficacy.