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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Oncology

Background:

  • Patient-reported side effects are crucial for cancer chemotherapy monitoring.
  • Manual extraction and normalization of this data are time-consuming and prone to errors.
  • Web applications offer a platform for remote monitoring of treatment adverse events.

Purpose of the Study:

  • To develop and evaluate deep learning models for automated extraction and normalization of free-text chemotherapy side effects.
  • To assess the performance of these models in a real-world remote monitoring application.
  • To lay the groundwork for integrating these methods into telemonitoring devices for clinical decision support.

Main Methods:

  • Utilized deep learning, specifically Bi-LSTM-CRF, for medical concept and negation extraction from patient-reported text.
  • Developed a normalization process to map extracted concepts to Unified Medical Language System (UMLS) concepts.
  • Evaluated model performance using F-measure for extraction and concept matching scores for normalization.

Main Results:

  • The medical concept extraction model achieved an F-measure of 0.79.
  • The negation extraction model (Bi-LSTM-CRF) achieved an F-measure of 0.85.
  • 62.3% of 1040 unique concepts achieved a perfect match (UMLS CUI score of 1) after normalization.

Conclusions:

  • Deep learning models demonstrate effectiveness in extracting and normalizing patient-reported chemotherapy side effects.
  • Current performance requires further improvement for seamless integration into home telemonitoring systems.
  • Future work should focus on enhancing model accuracy for automatic, real-time alerts to oncologists.