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Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Natural Language Processing

Background:

  • Accurate identification of cancer treatments is crucial for treatment planning and personalized medicine.
  • Existing methods for identifying cancer treatments from electronic medical records are often insufficient.
  • Leveraging both structured data and unstructured clinical notes can potentially improve treatment identification accuracy.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) approach for identifying initial cancer treatments.
  • To compare the performance of NLP models using structured data, unstructured clinical notes, and combinations thereof.
  • To assess the utility of this approach for cancer treatment cohort identification.

Main Methods:

  • Utilized a dataset of 4,412 patients with nonmetastatic prostate, oropharynx, and esophagus cancer from the Stanford Cancer Institute Research Database.
  • Trained NLP models using structured electronic medical records and unstructured clinical notes (bag-of-words, doc2vec, fasttext).
  • Compared performance of models using only structured data, only unstructured data, and combined data, optimizing among five machine learning methods.

Main Results:

  • Achieved high F1-scores for prostate cancer (0.99 for radiation, 1.00 for surgery) using structured + doc2vec.
  • Obtained F1-scores of 0.78 for oropharynx cancer chemoradiation and 0.83 for surgery using doc2vec.
  • Demonstrated that incorporating free-text clinical notes significantly outperforms billing codes or structured data alone across all cancer types studied.

Conclusions:

  • The developed NLP approach using free-text clinical notes substantially enhances cancer treatment identification accuracy.
  • This method offers a significant improvement over traditional approaches relying on billing codes or solely structured data.
  • The approach is adaptable for identifying cancer treatment cohorts and can be extended for longitudinal studies.