Medicine and Health Sciences Commons^™

Quantifying Disease Progression In Copd Patients By Forecasting Pft Scores Using Extracted Features From Non-Contrast Ct, A T. Luong, C J. Herrera, E Young, Y K. Liu, A Nowacki, J Cisneros-Paz, Girish Nair, E Castillo

Conference Presentation Abstracts

Purpose: Currently there is no validated method for quantifying risk of disease progression in chronic obstructive pulmonary disease (COPD). We aim to address this by predicting whether a patient will worsen using a machine learning model trained on basic patient information, current pulmonary function values, FEV1 and FVC, and extracted features from a non-contrast CT scan. Disease severity is characterized by these pulmonary function values, and by extension disease progression can be determined by the change in these values between future timepoints. Methods: XGBoost, a popular classification library that utilizes gradient boosted trees, was used to define an ensemble model …