Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables.

ABSTRACT:

Objectives

To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients.

Materials and methods

Our system was developed using a cohort from National Taiwan University Hospital in Taiwan, with external validation data from ASST Grande Ospedale Metropolitano Niguarda in Italy. Convolutional neural networks pretrained on ImageNet were regressively trained using a 5-point scale to develop the influenza chest X-ray (CXR) severity scoring model, FluDeep-XR. Early, late, and joint fusion structures, incorporating varying weights of CXR severity with clinical data, were designed to predict 30-day mortality and compared with models using onl

SUBMITTER: Tsai MH 

PROVIDER: S-EPMC12758471 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature