@inproceedings{3fc132f7c0e140b88181259ab96aa086,
title = "Machine learning methods to predict lung cancer survival using the Veterans Affairs Research precision oncology data commons",
abstract = "We completed a pilot study to guide the development of the VA Research Precision Oncology Data Commons infrastructure as a collaboration platform with the greater research community. Our results using a small subset of patients from the VA's Precision Oncology Program demonstrate the feasibility of our data sharing platform to build predictive models for lung cancer survival using machine learning, as well as highlight the potential of target genome sequencing data.",
keywords = "Lung Neoplasms, Machine Learning, Precision Medicine",
author = "Do, \{Nhan V.\} and Ramos, \{Jaime C.\} and Fillmore, \{Nathanael R.\} and Grossman, \{Robert L.\} and Michael Fitzsimons and Elbers, \{Danne C.\} and Frank Meng and Johnson, \{Brett R.\} and Samuel Ajjarapu and Dedomenico, \{Corri L.\} and Pierce-Murray, \{Karen E.\} and Hall, \{Robert B.\} and Do, \{Andrew F.\} and Kelly Gaynor and Elkin, \{Peter L.\} and Brophy, \{Mary T.\}",
note = "Publisher Copyright: {\textcopyright} 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",
year = "2019",
month = aug,
day = "21",
doi = "10.3233/SHTI190480",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1453",
editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",
booktitle = "MEDINFO 2019",
}