NCI Biomedical Informatics Blog
- The Promise and the Challenge of Deep Learning in Pathology
- Predictive Modeling for Pre-clinical Drug Screening: Improving Models Derived From Observational Studies Using Machine Learning and Simulation
- Population Level Pilot: Population Information Integration, Analysis, and Modeling for Precision Surveillance
- Introducing the Data Commons Framework
- Modeling the Dynamics of Membrane-bound Mutant RAS to Accelerate Discovery of Novel Drug Targets
JDACS4C Population Level Pilot for Population Information Integration, Analysis and Modeling
Goal: To apply computational and algorithmic advances to create a scalable framework for efficient abstraction, curation, integration and structuring of medical record information for cancer patients.
Description: This pilot aims to increase available information about cancer treatments beyond what is derived from clinical trials, to include the population, through improved efficiency of the SEER program research database. Efforts will explore methods to connect SEER data to electronic medical records, Census data, hospital and pharmacy data, patient-generated data, and the knowledge generated from the molecular- and cellular-level pilots. Further, improved access to SEER data will integrate additional sources of information to better capture real world insight into factors affecting cancer patient outcomes.
Lynne Penberthy (NCI, Division of Cancer Control and Population Sciences)
Paul Fearn (NCI, Division of Cancer Control and Population Sciences)
Anastasia Christianson (NCI, Frederick National Laboratory for Cancer Research)
Georgia Tourassi (DOE, Oak Ridge National Laboratory)
Gil Weigand (DOE, Oak Ridge National Laboratory)