Patterns (Feb 2021)
An interpretable deep-learning model for early prediction of sepsis in the emergency department
Abstract
Summary: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. Early prediction of sepsis improves survival in septic patients. In this paper, we report our top-performing method in the 2019 DII National Data Science Challenge to predict onset of sepsis 4 h before its diagnosis on electronic health records of over 100,000 unique patients in emergency departments. A long short-term memory (LSTM)-based model with event embedding and time encoding is leveraged to model clinical time series and boost prediction performance. Attention mechanism and global max pooling techniques are utilized to enable interpretation for the deep-learning model. Our model achieved an average area under the curve of 0.892 and was selected as one of the winners of the challenge for both prediction accuracy and clinical interpretability. This study paves the way for future intelligent clinical decision support, helping to deliver early, life-saving care to the bedside of septic patients. The bigger picture: Sepsis is the leading cause of death worldwide and has become a global epidemiological burden. Early prediction of sepsis enables early treatment and increases the likelihood of survival for septic patients. The broad adoption of electronic health records (EHRs) provides an opportunity for sepsis prediction. However, most existing prediction approaches do not consider irregular time intervals between neighboring clinical events in EHRs. Besides, many deep-learning models suffer from black-box problems and are not trusted in clinical settings. We propose a deep-learning model with time encodings, offering both high accuracy and high transparency as well as clinical interpretability. We have already made our code and its detailed documentations publicly available, enabling colleagues to apply it to their applications and eventually make clinical impacts.
