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Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predictrisk levels in patients with confirmed coronavirus disease 2019 (COVID-19) andto evaluate the performance of pre-trained fusion models on full or partialcombination of chest x-ray (CXRs) or chest radiograph and clinical variables.  Materials and Methods: The initial CXRs, clinical variables and outcomes(i.e., mortality, intubation, hospital length of stay, ICU admission) werecollected from February 2020 to April 2020 with reverse-transcriptionpolymerase chain reaction (RT-PCR) test results as the reference standard. Therisk level was determined by the outcome. The fusion model was trained on 1657patients (Age: 58.30 +/- 17.74; Female: 807) and validated on 428 patients(56.41 +/- 17.03; 190) from Northwestern Memorial HealthCare system and wastested on 439 patients (56.51 +/- 17.78; 205) from a single holdout hospital.Performance of pre-trained fusion models on full or partial modalities werecompared on the test set using the DeLong test for the area under the receiveroperating characteristic curve (AUC) and the McNemar test for accuracy,precision, recall and F1.  Results: The accuracy of DeepCOVID-Fuse trained on CXRs and clinicalvariables is 0.658, with an AUC of 0.842, which significantly outperformed (p <0.05) models trained only on CXRs with an accuracy of 0.621 and AUC of 0.807and only on clinical variables with an accuracy of 0.440 and AUC of 0.502. Thepre-trained fusion model with only CXRs as input increases accuracy to 0.632and AUC to 0.813 and with only clinical variables as input increases accuracyto 0.539 and AUC to 0.733.  Conclusion: The fusion model learns better feature representations acrossdifferent modalities during training and achieves good outcome predictions evenwhen only some of the modalities are used in testing.

DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels