  Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predict
risk levels in patients with confirmed coronavirus disease 2019 (COVID-19) and
to evaluate the performance of pre-trained fusion models on full or partial
combination 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) were
collected from February 2020 to April 2020 with reverse-transcription
polymerase chain reaction (RT-PCR) test results as the reference standard. The
risk level was determined by the outcome. The fusion model was trained on 1657
patients (Age: 58.30 +/- 17.74; Female: 807) and validated on 428 patients
(56.41 +/- 17.03; 190) from Northwestern Memorial HealthCare system and was
tested on 439 patients (56.51 +/- 17.78; 205) from a single holdout hospital.
Performance of pre-trained fusion models on full or partial modalities were
compared on the test set using the DeLong test for the area under the receiver
operating characteristic curve (AUC) and the McNemar test for accuracy,
precision, recall and F1.
  Results: The accuracy of DeepCOVID-Fuse trained on CXRs and clinical
variables 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 0.807
and only on clinical variables with an accuracy of 0.440 and AUC of 0.502. The
pre-trained fusion model with only CXRs as input increases accuracy to 0.632
and AUC to 0.813 and with only clinical variables as input increases accuracy
to 0.539 and AUC to 0.733.
  Conclusion: The fusion model learns better feature representations across
different modalities during training and achieves good outcome predictions even
when only some of the modalities are used in testing.
