Multimodal deep learning for predicting mechanical ventilation duration from chest X-ray and clinical data

Abstract

In critical care facilities, invasive mechanical ventilation is now a key criterion for determining the severity of illness caused by the COVID-19 pandemic. Both clinical decision processes and the efficient use of critical care facilities can be improved by using the predicted duration of invasive ventilation. In order to predict the invasive ventilator days, the study proposes the use of a multimodal learning architecture that utilizes both clinical data and chest X-ray images.The Cancer Imaging Archive (TCIA) dataset included chest X-ray images and clinical data for patients. A total of 213 full instances were retained after rigorous data cleansing and mapping using patient identifiers. Z-score normalization was used to normalize the clinical data, and intensity normalization and scaling were used to normalize the chest X-ray DICOM image data. The study used a continuous regression model to predict the ventilation day outcome.The study used a variety of deep learning and machine learning models, including ResNet-18, DenseNet-121, Random Forest, Linear Regression, and Gradient Boosting. A multimodal attention-based fusion model was used to combine the image and clinical data. The study used MAE, RMSE, R², Pearson correlation, and Spearman correlation to evaluate the model’s performance. The results of the experiments show that multimodal deep-learning models have moderate performance, and the ensemble-based clinical models perform better than the linear ones. Besides, the results highlight the significant role of clinical data, and image data provides marginal performance improvements.