Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records - Savova, Guergana K. (Cancer Res, 2019)
We survey advances in information extraction from the free-text of electronic medical records as related to the complex domain of oncology. Current models for correlating Electronic Medical Records with –omics data largely ignore the clinical text, which remains an important source of phenotype information for cancer patients. This data convergence has the potential to enable 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 methods are needed to extract these rich cancer phenotypes from the clinical text. We review the advances of natural language processing and information extraction methods relevant to oncology since the Yim et al, 2016 paper in JAMA Oncology. The current survey is based on publications from PubMed as well as NLP and machine learning conference proceedings. Because of this broad catchment, the survey summarizes the main trends in natural language processing and information extraction for oncology over the last 3 years organized by task and application. Given the interdisciplinary nature of the fields of oncology and information extraction, this survey serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.