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.

翻译：数据缺失与质量问题是机器学习中的常见难题，尤其在医疗等高风险应用中更为突出。开发者通常仅使用高质量数据在精心策划的数据集上训练模型，但这会降低模型在生产环境中的实用性。我们提出一种新型神经网络改进方法，通过将全连接层的固定权重替换为附加输入的函数，来减轻低质量与缺失数据的影响。这一设计受生物神经网络中的神经调制机制启发——大脑皮层可根据输入信号的可靠性及其他数据的存在对其进行上/下调节。在测试中，将可靠度评分作为调制信号时，具有调制层的模型对数据质量退化（包括额外缺失）表现出更强的鲁棒性。此类模型优于插补方法，原因在于其完全省略插补过程从而节省训练时间，且能引入插补无法处理的其他数据质量指标。实验结果表明，通过调制全连接层显式应对信息质量下降问题，可推动人工智能系统在实时应用中的部署。