Cancer Deep Phenotype Extraction from Electronic Medical Records

Faculty Role: 
Grant Faculty: 
Grant Role: 
Grant Role: 
Co-Principal Investigator
Funding Agency: 
Grant/Contract No.: 
1 U24 CA184407-01
Project Dates: 
05/01/2014 to 04/30/2019

Precise phenotype information is needed to advance translational cancer research, particularly to unravel the effects of genetic, epigenetic, and other factors on tumor behavior and responsiveness. Current models for correlating EMR data with -omics data largely ignore the clinical text, which remains one of the most important sources of phenotype information for cancer patients. Unlocking the value of clinical text has the potential to enable new insights about cancer initiation, progression, metastasis, and response to treatment. We propose further collaboration of two mature informatics groups with long histories of developing open-source natural language processing (NLP) software (Apache cTAKES, caTIES and ODIE) to extend existing software with new methods for cancer deep phenotyping