  Data missingness and quality are common problems in machine learning,
especially for high-stakes applications such as healthcare. Developers often
train machine learning models on carefully curated datasets using only high
quality data; however, this reduces the utility of such models in production
environments. We propose a novel neural network modification to mitigate the
impacts of low quality and missing data which involves replacing the fixed
weights of a fully-connected layer with a function of an additional input. This
is inspired from neuromodulation in biological neural networks where the cortex
can up- and down-regulate inputs based on their reliability and the presence of
other data. In testing, with reliability scores as a modulating signal, models
with modulating layers were found to be more robust against degradation of data
quality, including additional missingness. These models are superior to
imputation as they save on training time by completely skipping the imputation
process and further allow the introduction of other data quality measures that
imputation cannot handle. Our results suggest that explicitly accounting for
reduced information quality with a modulating fully connected layer can enable
the deployment of artificial intelligence systems in real-time applications.
