  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) for the detection of COVID-19 using laboratory tests
and chest x-rays. 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 architecture 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 collected includes
results from 27 laboratory tests and a chest x-ray scored by a deep learning
network. Training and test datasets are defined based on the source medical
facility. 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. Using area under the
receiver operating curve (AUC) as a metric, 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 achieved by any other model. Furthermore, with clinically
meaningful subsets of parameters consisting of just 6 and 7 inputs
respectively, DFCN also achieves higher AUCs than any other model, with values
of 0.909 and 0.919.
