Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[A Transformer-based multimodal model for predicting hospital-acquired infections using imaging and clinical laboratory data].
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Zhang Mingxu, Wu Diheng, Lin Jiaze, Lü Yijun, Zhou Linghong, Zhen Xin, Zhou Hao, Qin Genggeng
Аннотация
OBJECTIVES: To construct a Transformer-based multimodal data encoding model for predicting hospital-acquired infections (HAI).
METHODS: Laboratory test data of 300 000 patients were extracted from the publicly available MIMIC-IV database. The laboratory data of 1172 patients and chest X-ray images from 274 of these patients were collected from Nanfang Hospital. A novel Transformer-based encoding model was developed to process the data, which was then connected to a machine learning classifier for predicting HAI. The radiomic and deep learning features were extracted from the chest X-ray images for predicting ventilator-associated pneumonia (VAP). These imaging features were subsequently integrated with the laboratory test data using a feature fusion algorithm. The model performance was evaluated by assessing the accuracy, the area under the ROC curve (AUC), sensitivity, and specificity. The proposed algorithm was quantitatively compared against traditional machine learning classifiers to validate its effectiveness and feasibility.
RESULTS: The results demonstrated that the model developed in this study achieved an AUC of 0.989 in the internal validation set. In the external validation set, the optimal model for predicting HAI attained an AUC of 0.98, and following the integration of imaging features, the optimal model reached an AUC of 0.93 in the VAP prediction task, demonstrating superior performance over the baseline models.
CONCLUSIONS: The Transformer-based model for processing laboratory test data has excellent predictive capability and good clinical applicability for HAI prediction with also good performance for predicting VAP.
METHODS: Laboratory test data of 300 000 patients were extracted from the publicly available MIMIC-IV database. The laboratory data of 1172 patients and chest X-ray images from 274 of these patients were collected from Nanfang Hospital. A novel Transformer-based encoding model was developed to process the data, which was then connected to a machine learning classifier for predicting HAI. The radiomic and deep learning features were extracted from the chest X-ray images for predicting ventilator-associated pneumonia (VAP). These imaging features were subsequently integrated with the laboratory test data using a feature fusion algorithm. The model performance was evaluated by assessing the accuracy, the area under the ROC curve (AUC), sensitivity, and specificity. The proposed algorithm was quantitatively compared against traditional machine learning classifiers to validate its effectiveness and feasibility.
RESULTS: The results demonstrated that the model developed in this study achieved an AUC of 0.989 in the internal validation set. In the external validation set, the optimal model for predicting HAI attained an AUC of 0.98, and following the integration of imaging features, the optimal model reached an AUC of 0.93 in the VAP prediction task, demonstrating superior performance over the baseline models.
CONCLUSIONS: The Transformer-based model for processing laboratory test data has excellent predictive capability and good clinical applicability for HAI prediction with also good performance for predicting VAP.