Interpretable machine learning models for lung cancer screening
Principal Investigator
Name
Cynthia Rudin
Degrees
PhD
Institution
Duke
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-332
Initial CDAS Request Approval
Aug 2, 2017
Title
Interpretable machine learning models for lung cancer screening
Summary
The goal of my lab is to produce interpretable machine learning models and test them on publicly available data. We hope to provide clinical tools for lung cancer screening using machine learning where the algorithms not only provide predictions but also explanations for how the predictions were made.
Aims
- Create machine learning tools for interpretable modeling
- Test them on publicly available data
- Work with clinicians to develop measures of interpretability specific to lung cancer screening
Collaborators
Amir Tahmasebi - Philips Amir.Tahmasebi@philips.com
Omar Badawi - Philips - omar.badawi@philips.com
Reza Sharifi Sedeh - Philips - reza.sharifi.sedeh@philips.com
Hongyu Yang - MIT - hongyuy@mit.edu