An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis

Das, Pronaya Prosun; Wiese, Lena; Mast, Marcel; Böhnke, Julia; Wulff, Antje; Marschollek, Michael; Bode, Louisa; Rathert, Henning; Jack, Thomas; Schamer, Sven; Beerbaum, Philipp; Rübsamen, Nicole; Karch, André; Groszweski-Anders, Christian; Haller, Andreas; Frank, Thorsten

Abstract

Sepsis is a severe and expensive medical emergency that requires prompt identification in order to improve patient mortality. The objective of our research is to develop an attention-based bidirectional LSTM-CNN (AT-BiLSTM-CNN) hybrid architecture for the early prediction of sepsis using electronic health records (EHRs) obtained from intensive care units (ICUs). We combine attention mechanism, bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) to analyse clinical time series data, aiming to enhance prediction accuracy. The effectiveness of our model is measured using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC), utilising data from the 2019 PhysioNet Challenge. Upon assessing the performance of the AT-BiLSTM-CNN model throughout prediction windows of 4, 8, and 12 h, we observed its exceptional performance in comparison with existing leading techniques. It achieved average AUROCs of 0.88, 0.85, and 0.84 for the predictions made 4, 8, and 12 h before sepsis onset, respectively. This research contributes significantly to the development of smart clinical support systems, potentially offering lifesaving interventions for septic patients at critical moments.