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Machine learning to predict risk from chest radiographs and clinical factors

Principal Investigator
Name
Michael Lu
Degrees
M.D., M.P.H.
Institution
The General Hospital Corporation d/b/a Massachusetts General Hospital
Position Title
Co-Director, Cardiovascular Imaging Research Center
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-383
Initial CDAS Request Approval
Dec 29, 2017
Title
Machine learning to predict risk from chest radiographs and clinical factors
Summary
Heart disease and cancer are the two most common causes of death in the developed world. NLST participants underwent screening chest radiography; analysis of these chest radiographs may be useful to predict incident morbidity and mortality. The goal of this proposal is to use machine learning to predict the risk of cancer, heart disease, and death from chest radiographs and other clinical information available in the NLST. Whether this approach is additive to existing risk prediction models will be assessed via questionnaire data about patient risk factors and medical history.
Aims

1) To predict cancer, heart disease, and death from chest radiographs using machine learning.
2) To compare the performance of this approach to existing risk prediction tools.

Collaborators

Parastou Eslami (Harvard)
Borek Foldyna (Harvard)
Udo Hoffmann (Harvard)
Alexander Ivanov (Harvard)
Chintan Parmar (Harvard)
Roman Zeleznik (Harvard)

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