Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references

ConclusionsThe results point toward machine learning models such as RF have the potential to improve the prediction of estimated lung function for individual patients. These predictions are used as reference values and are an important part of assessing spirometry measurements in clinical practice. Further work is necessary in order to reduce the size of the intercepts obtained through these results.

Source: The Clinical Respiratory Journal - July 14, 2023 Category: Respiratory Medicine Authors: Kris Kristensen, Pernille H. Olesen, Anna K. Roerbaek, Louise Nielsen, Helle K. Hansen, Simon L. Cichosz, Morten H. Jensen, Ole Hejlesen Tags: LETTER TO THE EDITOR Source Type: research