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Review
. 2019 Nov 1;79(21):5463-5470.
doi: 10.1158/0008-5472.CAN-19-0579. Epub 2019 Aug 8.

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Guergana K Savova  1   2 , Ioana Danciu  3 , Folami Alamudun  3 , Timothy Miller  4   2 , Chen Lin  4 , Danielle S Bitterman  2   5 , Georgia Tourassi  3 , Jeremy L Warner  6
Affiliations
Review

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Guergana K Savova et al. Cancer Res. .

Abstract

Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.

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Conflict of interest statement

Conflict of Interest Statement: none

Figures

Figure 1.
Figure 1.
Clinical TempEval example: two events, one time expression, two temporal relations, two relations to the document creation time (DocTimeRel).
See this image and copyright information in PMC

References

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