Cited by 298Biomedical Text Mining and OntologiesMachine Learning in HealthcareElectronic Health Records Systems

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Natural Language Processing in Oncology: A Review

Yim, Wen-wai, Yetisgen, Meliha, Harris, William P., Kwan, Sharon W.·2016·JAMA Oncology

DOI 10.1001/jamaoncol.2016.0213

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Abstract

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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Authors: Yim, W.-W., Yetisgen, M., Harris, W. P., & Kwan, S. W.
Affiliations: University of Washington Medical Center (US)
Departments of Biomedical and Health Informatics, University of Washington Medical Center, Seattle2Departments of Linguistics, University of Washington Medical Center, Seattle
ORCID: Yim, Wen-wai
Harris, William P.
Kwan, Sharon W.
Venue: JAMA Oncology
Publisher: American Medical Association (AMA)
Published: 2016-06-01
Volume: 2
Issue: 6
Pages: 797
ISSN: 2374-2437 (ISSN-L)
DOI: 10.1001/jamaoncol.2016.0213
OpenAlex: W2345195116
Cited by: 298
References: 59
Citations by year: 2026: 23 · 2025: 40 · 2024: 38 · 2023: 44 · 2022: 35 · 2021: 40 · 2020: 33 · 2019: 23 · 2018: 11 · 2017: 10 · 2016: 1
FWCI: 7.79
Type: journal article
Language: en
Open access: closed
Locations: JAMA Oncology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: natural language processing, oncology, clinical natural language processing, clinical text, electronic health records, tumor staging, evidence-based oncology, quality improvement, personalized medicine, clinical research tools
MeSH terms: Humans, Natural Language Processing, Neoplasms, Biomedical Research, Translational Research, Biomedical, Electronic Health Records

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