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Area of Science:

  • Oncology
  • Medical Informatics
  • Computational Linguistics

Background:

  • Electronic medical records contain unstructured text with valuable clinical variables.
  • Personalized medicine and increasing therapeutic options necessitate efficient data extraction.
  • Natural language processing (NLP) offers a solution for leveraging this unstructured data.

Purpose of the Study:

  • To introduce Natural Language Processing (NLP) and its applications in oncology.
  • To describe available NLP tools for clinical research.
  • To review the current state of NLP in cancer case identification, staging, and outcomes quantification.

Main Methods:

  • Review of NLP technology and its potential in oncology.
  • Description of specific NLP tools.
  • Analysis of NLP's role in extracting clinical variables from free-text narratives.

Main Results:

  • NLP can harvest important clinical variables from electronic medical records.
  • NLP serves as a tool for oncological evidence-based research and quality improvement.
  • Research-minded oncologists can contribute to NLP development and application in cancer care.

Conclusions:

  • Automated leveraging of unstructured data is crucial for advancing cancer care.
  • Clinical NLP enables research-minded oncologists to enhance evidence-based research.
  • Continued progress in NLP for oncology will lead to significant impacts and cost-effective cancer care infrastructure.