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June 2016

Natural Language Processing in Oncology: A Review

Author Affiliations
  • 1Departments of Biomedical and Health Informatics, University of Washington Medical Center, Seattle
  • 2Departments of Linguistics, University of Washington Medical Center, Seattle
  • 3Division of Oncology, Departments of Medicine, University of Washington Medical Center, Seattle
  • 4Department of Radiology, University of Washington Medical Center, Seattle
JAMA Oncol. 2016;2(6):797-804. doi:10.1001/jamaoncol.2016.0213

Importance  Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records.

Observations  Natural language processing can be used as a tool for oncological evidence-based research and quality improvement. Oncologists interested in applying NLP for clinical research can play pivotal roles in building NLP systems and, in doing so, contribute to both oncological and clinical NLP research. Herein, we provide an introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification.

Conclusions and Relevance  More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.

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