External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries.

ABSTRACT: Precise and timely referral for lung transplantation is critical for the survival of cystic fibrosis patients with terminal illness. While machine learning (ML) models have been shown to achieve significant improvement in prognostic accuracy over current referral guidelines, the external validity of these models and their resulting referral policies has not been fully investigated. Here, we studied the external validity of machine learning-based prognostic models using annual follow-up data from the UK and Canadian Cystic Fibrosis Registries. Using a state-of-the-art automated ML framework, we derived a model for predicting poor clinical outcomes in patients enrolled in the UK registry, and conducted external validation of the derived model using the Canadian Cystic Fibrosis Registry. In p

SUBMITTER: Qin Y 

PROVIDER: S-EPMC9931238 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature