Automated Real-World Data Integration Improves Cancer Outcome Prediction
To Know
Where:
Online
Organizer:
CBIIT
Presented By:
Nikolaus Schultz, PhD (Computational Biology Center, Memorial Sloan-Kettering Cancer Center)
About this Class
If you’re interested in learning how machine learning can help you tap into the rich data within electronic health records, join us to learn more about the “Memorial Sloan Kettering (MSK) Clinicogenomic Harmonized Oncologic Real-World Data set” otherwise known as MSK-CHORD. It includes data for over 25,000 cancer patients to help you identify clinicogenomic relationships that aren’t as obvious in smaller, siloed data sets.
Dr. Nikolaus Schultz, an NCI grantee and computational oncologist at MSK Cancer Center, will give an overview of the data set and demonstrate:
- the feasibility of MSK-CHORD’s automated annotation from unstructured notes.
- how MSK-CHORD can train machine learning models to predict patient outcomes.
Some of the resulting data from studies leveraging MSK-CHORD are available via cBioPortal.