How much data should we collect? A case study in sepsis detection using deep learning

Abstract

Sepsis is an acute, life-threatening condition that results from bacterial infections, often acquired in the hospital. Undetected, sepsis can progress to severe sepsis and septic shock, with a risk of death as high as 30% to 80%. Early detection of sepsis can improve patient outcomes. Collecting and evaluating continuous physiological variables, such as vital signs, using sophisticated classification algorithms may be highly beneficial to aid diagnosis of septic patients. However, setting up a data acquisition system that can collect (and store) high frequency/high volume data is challenging both from technology management and storage standpoints. In this paper, we build two deep learning models, a convolutional neural network and a multilayer perceptron model, to classify patients into sepsis and non-sepsis groups using data collected at various frequencies from the first 12 hours after admission. Our results indicate that the convolutional neural network model outperforms the multilayer perceptron model for all data collection frequencies. In addition, our results put into perspective the value of data collection frequency and translate its value into lives saved. Such analysis can guide future investments in data acquisition systems by hospitals.