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Background: The predictive Intensive Care Unit (ICU) scoring system plays animportant role in ICU management because it predicts important outcomes,especially mortality. Many scoring systems have been developed and used in theICU. These scoring systems are primarily based on the structured clinical datain the electronic health record (EHR), which may suffer the loss of importantclinical information in the narratives and images. Methods: In this work, webuild a deep learning based survival prediction model with multi-modality datato predict ICU mortality. Four sets of features are investigated: (1)physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2)common thorax diseases pre-defined by radiologists, (3) BERT-based textrepresentations, and (4) chest X-ray image features. We use the MedicalInformation Mart for Intensive Care IV (MIMIC-IV) dataset to evaluate theproposed model. Results: Our model achieves the average C-index of 0.7829 (95%confidence interval, 0.7620-0.8038), which substantially exceeds that of thebaseline with SAPS-II features (0.7470 (0.7263-0.7676)). Ablation studiesfurther demonstrate the contributions of pre-defined labels (2.00%), textfeatures (2.44%), and image features (2.82%).

An empirical study of using radiology reports and images to improve ICU mortality prediction