tetano
Editor, Senior Moderator
J Am Med Inform Assoc
. 2024 Nov 22
cae286.
doi: 10.1093/jamia/ocae286. Online ahead of print. Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables
Meng-Han Tsai[SUP] 1 [/SUP], Sung-Chu Ko[SUP] 2 [/SUP], Amy Huaishiuan Huang[SUP] 1 3 [/SUP], Lorenzo Porta[SUP] 4 5 [/SUP], Cecilia Ferretti[SUP] 6 [/SUP], Clarissa Longhi[SUP] 6 [/SUP], Wan-Ting Hsu[SUP] 7 [/SUP], Yung-Han Chang[SUP] 8 [/SUP], Jo-Ching Hsiung[SUP] 9 [/SUP], Chin-Hua Su[SUP] 5 [/SUP], Filippo Galbiati[SUP] 10 [/SUP], Chien-Chang Lee[SUP] 1 5 11 [/SUP]
Affiliations
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.
Keywords: FluDeep; artificial intelligence; clinical informatics; influenza; multimodal model.
. 2024 Nov 22
cae286.doi: 10.1093/jamia/ocae286. Online ahead of print. Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables
Meng-Han Tsai[SUP] 1 [/SUP], Sung-Chu Ko[SUP] 2 [/SUP], Amy Huaishiuan Huang[SUP] 1 3 [/SUP], Lorenzo Porta[SUP] 4 5 [/SUP], Cecilia Ferretti[SUP] 6 [/SUP], Clarissa Longhi[SUP] 6 [/SUP], Wan-Ting Hsu[SUP] 7 [/SUP], Yung-Han Chang[SUP] 8 [/SUP], Jo-Ching Hsiung[SUP] 9 [/SUP], Chin-Hua Su[SUP] 5 [/SUP], Filippo Galbiati[SUP] 10 [/SUP], Chien-Chang Lee[SUP] 1 5 11 [/SUP]
Affiliations
- PMID: 39576664
- DOI: 10.1093/jamia/ocae286
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.
Keywords: FluDeep; artificial intelligence; clinical informatics; influenza; multimodal model.