Novel deep learning architectures for haemodialysis time series classification

Abstract

Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. In particular, we have defined two novel architectures, able to take advantage of the strengths of Convolutional Neural Networks and of Recurrent Networks. The novel architectures we introduced and tested outperformed classical mathematical classification techniques, as well as simpler deep learning approaches. In particular, combining Recurrent Networks with convolutional structures in different ways, allowed us to obtain accuracies above 81%, coupled with high values of the Matthews Correlation Coefficient (MCC), a parameter particularly suitable to assess the quality of classification when dealing with unbalanced classes-as it was our case. In the future we will test an extension of the approach to additional monitoring time series, aiming at an overall optimization of patient care.