Multitask neural networks for predicting bladder pressure with time series data

Abstract Multitask learning (MTL) can improve accuracy over vanilla neural networks in modeling population level time series data. This can be accomplished by assigning the prediction for each individual in the population as a separate task, thereby leveraging the heterogeneity of population level data. Here, we investigate a novel approach by training recurrent neural networks (RNNs) in a multitask setting. We apply this new methodology to experimental data for predicting bladder pressure, and then bladder contractions, from an external urethral sphincter electromyograph (EUS EMG) signal. We found that the multitask models make more accurate individual level predictions than their single tasking counterparts. We observed that, for bladder pressure prediction, either incorporating multitask learning or RNN structure generalized best to out of sample test data and multitasking RNNs had high out of sample correlation coefficients. These results suggest that MTL models could be used to leverage heterogeneous population time series data for making individualized predictions. From these bladder pressure predictions, we predicted the onset of bladder contractions. Our results indicate that the MTL RNN model was superior in both intra- and inter-individual bladder contraction predictions as measured by sensitivity (85.7%), specificity (98.7%) and precision (73.5%).