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 only CXR or clinical data. The best-performing model was designated as FluDeep. The explainability of FluDeep-XR and FluDeep was illustrated through activation maps and SHapley Additive exPlanations (SHAP).

Results: The Xception-based model, FluDeep-XR, achieved a mean square error of 0.738 in the external validation dataset. The Random Forest-based late fusion model, FluDeep, outperformed all the other models, achieving an area under the receiver operating curve of 0.818 and a sensitivity of 0.706 in the external dataset. Activation maps highlighted clear lung fields. Shapley additive explanations identified age, C-reactive protein, hematocrit, heart rate, and respiratory rate as the top 5 important clinical features.

Discussion: The integration of medical imaging with objective clinical data outperformed single-modality models to predict 30-day mortality in influenza patients. We ensured the explainability of our models aligned with clinical knowledge and validated its applicability across foreign institutions.

Conclusion: FluDeep highlights the potential of combining radiological and clinical information in late fusion design, enhancing diagnostic accuracy and offering an explainable, and generalizable decision support system.