• Guergana K Savova, Ioana Danciu, Folami Alamudun, Timothy Miller, Chen Lin, Danielle S Bitterman, Georgia Tourassi, and Jeremy L Warner.
• Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts. Guergana.Savova@childrens.harvard.edu.
• Cancer Res. 2019 Nov 1; 79 (21): 5463-5470.

AbstractCurrent 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.©2019 American Association for Cancer Research.

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