Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records
Year: 2019
Type: review
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.
Source: Cancer Research
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Network Analysis for a Data-Driven Approach to Cancer Care, Cancer Deep Phenotype Extraction from Electronic Medical Records, Extended Methods and Software Development for Health NLP, Natural Language Processing Platform for Cancer Surveillance, G675320460, Temporal relation discovery for clinical text, Cancer Deep Phenotype Extraction from Electronic Medical Records (renewal)
OpenAlex ID: W2967056613