  In the context of the current global pandemic and the limitations of the
RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising
Fully Connected Network). Since medical facilities around the world differ
enormously in what laboratory tests or chest imaging may be available, DFCN is
designed to be robust to missing input data. An ablation study extensively
evaluates the performance benefits of the DFCN as well as its robustness to
missing inputs. Data from 1088 patients with confirmed RT-PCR results are
obtained from two independent medical facilities. The data includes results
from 27 laboratory tests and a chest x-ray scored by a deep learning model.
Training and test datasets are taken from different medical facilities. Data is
made publicly available. The performance of DFCN in predicting the RT-PCR
result is compared with 3 related architectures as well as a Random Forest
baseline. All models are trained with varying levels of masked input data to
encourage robustness to missing inputs. Missing data is simulated at test time
by masking inputs randomly. DFCN outperforms all other models with statistical
significance using random subsets of input data with 2-27 available inputs.
When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any
other model. Furthermore, with clinically meaningful subsets of parameters
consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than
any other model, with values of 0.909 and 0.919.
