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

A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues