RT Journal Article SR Electronic T1 Machine learning tools to detect COPD patients with Excessive Sedentary living and Physical Inactivity JF European Respiratory Journal JO Eur Respir J FD European Respiratory Society SP 4156 DO 10.1183/13993003.congress-2020.4156 VO 56 IS suppl 64 A1 Bernard Aguilaniu A1 David Hess A1 Bruno Degano A1 Christophe Pison A1 Anestis Antoniadis YR 2020 UL http://erj.ersjournals.com/content/56/suppl_64/4156.abstract AB Based on the hypothesis that many continuous and categorical variables collected during the consultation could be affected (as cause or consequence) by excessively sedentary and physically inactive behaviour (ESPI), we developed a predictive method based on all the information collected during the COPD digital consultation (n=5040). The variable to be predicted, i.e. the ESPI, was defined by crossing 2 estimates among several categories of activities proposed to the doctor and the patient. At the other end of the spectrum, we defined also an ACTIVE status.Methods: we first verified that the ESPI variable and its variability were well correlated with a set of continuous and categorical variables measured by performing a factor analysis on mixed data. A scree plot was then used to determine the statistically significant factors of the components to be included in more elaborate individual predictive models. To ensure the robustness of the prediction, we compared two predictive models (a multiple logistic regression and a random forest), and to generalize their predictive power through estimates of the uncertainty of their predictions, these models were improved by sequentially adding several random effects such as the identity of the doctor, the hospital centre, etc. The models were then used to estimate the uncertainty of the predictions.Results: The set of methods used allows to predict the ESPI and ACTIVE status with an error rate of 33% and 7% respectively.Conclusion: This proof-of-concept study demonstrates the ability to detect ESPI status using a machine learning powered by common variables from a digital consultation including a wide range of COPD patients.FootnotesCite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4156.This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).