Use of natural language processing to extract clinical cancer phenotypes from electronic medical records

Guergana K. Savova; Ioana Danciu; Folami Alamudun; Timothy Miller; Chen Lin; Danielle S. Bitterman; Georgia Tourassi; Jeremy L. Warner

doi:10.1158/0008-5472.CAN-19-0579

Use of natural language processing to extract clinical cancer phenotypes from electronic medical records

Guergana K. Savova
, Ioana Danciu
, Folami Alamudun
, Timothy Miller
, Chen Lin
, Danielle S. Bitterman
, Georgia Tourassi
, Jeremy L. Warner

Biostatistics and Biomedical Informatics

Research output: Contribution to journal › Review article › peer-review

128 Scopus citations

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, wereview 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.

Original language	English
Pages (from-to)	5463-5470
Number of pages	8
Journal	Cancer Research
Volume	79
Issue number	21
DOIs	https://doi.org/10.1158/0008-5472.CAN-19-0579
State	Published - Nov 1 2019

Funding

The work was supported by funding from U24CA184407 (NCI), U01CA231840 (NCI), R01 LM 10090 (LM), and R01GM114355 (NIGMS). This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of National Institutes of Health. This work was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, and Oak Ridge National Laboratory under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). The work was supported by funding from U24CA184407 (NCI), U01CA231840 (NCI), R01 LM 10090 (LM), and R01GM114355 (NIGMS). This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of National Institutes of Health. This work was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, and Oak Ridge National Laboratory under Contract DE-AC05-00OR22725. This manuscript has been authored by UTBattelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of themanuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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10.1158/0008-5472.CAN-19-0579

Cite this

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title = "Use of natural language processing to extract clinical cancer phenotypes from electronic medical records",

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, wereview 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.",

author = "Savova, \{Guergana K.\} and Ioana Danciu and Folami Alamudun and Timothy Miller and Chen Lin and Bitterman, \{Danielle S.\} and Georgia Tourassi and Warner, \{Jeremy L.\}",

note = "Publisher Copyright: {\textcopyright} 2019 by The American Society of Hematology.",

year = "2019",

month = nov,

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doi = "10.1158/0008-5472.CAN-19-0579",

language = "English",

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T1 - Use of natural language processing to extract clinical cancer phenotypes from electronic medical records

AU - Savova, Guergana K.

AU - Danciu, Ioana

AU - Alamudun, Folami

AU - Miller, Timothy

AU - Lin, Chen

AU - Bitterman, Danielle S.

AU - Tourassi, Georgia

AU - Warner, Jeremy L.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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, wereview 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.

AB - 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, wereview 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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DO - 10.1158/0008-5472.CAN-19-0579

M3 - Review article

C2 - 31395609

AN - SCOPUS:85074445416

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ER -

Use of natural language processing to extract clinical cancer phenotypes from electronic medical records

Abstract

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