Multi-Channel ConvNet Approach to Predict the Risk of In-Hospital Mortality for ICU Patients

Presented at International Conference on Deep Learning Theory and Applications (DeLTA) 2020AbstractThe healthcare arena has been undergoing impressive transformations thanks to advances in the capacity to capture, store, process, and learn from data. This paper re-visits the problem of predicting the risk of in-hospital mortality based on Time Series (TS) records emanating from ICU monitoring devices. The problem basically represents an application of multi-variate TS classification. Our approach is based on utilizing multiple channels of Convolutional Neural Networks (ConvNets) in parallel. The key idea is to disaggregate multi-variate TS into separate channels, where a ConvNet is used to extract features from each univariate TS individually. Subsequently, the features extracted are concatenated altogether into a single vector that can be fed into a standard MLP classification module. The approach was experimented using a dataset extracted from the MIMIC-III database, which included about 13K ICU-related records. Our experimental results show a promising accuracy of classification that is competitive to the state-of-the-art.