Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease.

ABSTRACT: Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We sel

SUBMITTER: Jo YS 

PROVIDER: S-EPMC10618439 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature