Machine Learning Methods to Predict Lung Cancer Survival Using the Veterans Affairs Research Precision Oncology Data
Nhan V Do1,2, Jaime C Ramos1, Nathanael R Fillmore1
1MAVERIC, Department of Veterans Affairs Medical Center, Boston, MA, USA.
Studies in Health Technology and Informatics
|August 24, 2019
Summary
A pilot study shows a VA data sharing platform can build lung cancer survival models using machine learning and genomic data. This demonstrates the feasibility of the Precision Oncology Data Commons for research collaboration.
Area of Science:
- Oncology
- Bioinformatics
- Data Science
Background:
- The Veterans Affairs (VA) Precision Oncology Program aims to improve cancer care through personalized treatments.
- Developing robust data infrastructure is crucial for advancing precision oncology research.
Purpose of the Study:
- To guide the development of the VA Research Precision Oncology Data Commons.
- To assess the feasibility of a collaborative data sharing platform for oncology research.
- To explore the utility of machine learning and genomic data for predicting lung cancer survival.
Main Methods:
- A pilot study was conducted using a subset of patient data from the VA's Precision Oncology Program.
- Machine learning models were developed to predict lung cancer survival.
- Target genome sequencing data was analyzed.
Main Results:
- The study demonstrated the feasibility of the data sharing platform for building predictive models.
- Machine learning approaches showed potential in predicting lung cancer survival.
- Target genome sequencing data provided valuable insights.
Conclusions:
- The VA Research Precision Oncology Data Commons infrastructure is feasible as a collaborative research platform.
- The platform has the potential to advance lung cancer survival prediction and precision oncology.
- Genomic data is a key component for developing effective predictive models.
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