SSP: Early prediction of sepsis using fully connected LSTM-CNN model. (January 2021)

• Record Type: Journal Article
• Authors: Rafiei, Alireza; Rezaee, Alireza; Hajati, Farshid; Gheisari, Soheila; Golzan, Mojtaba
• Is Part Of: Computers in biology and medicine. Volume 128(2021)
• Publication Date: 2021-01
• Subjects: Sepsis -- Deep learning -- Early prediction -- Clinical decision support systems -- Electronic health records -- Medical informatic; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104110
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote the survival of suspected patients. However, reliable and intelligent systems for predicting sepsis are scarce. Methods: This paper presents a novel technique called Smart Sepsis Predictor (SSP) to predict sepsis onset in patients admitted to an intensive care unit (ICU). SSP is a deep neural network architecture that encompasses long short-term memory (LSTM), convolutional, and fully connected layers to achieve early prediction of sepsis. SSP can work in two modes; Mode 1 uses demographic data and vital signs, and Mode 2 uses laboratory test results in addition to demographic data and vital signs. To evaluate SSP, we have used the 2019 PhysioNet/CinC Challenge dataset, which includes the records of 40, 366 patients admitted to the ICU. Results: To compare SSP with existing state-of-the-art methods, we have measured the accuracy of the SSP in 4-, 8-, and 12-h prediction windows using publicly available data. Our results show that the SSP performance in Mode 1 and Mode 2 is much higher than existing methods, achieving an area under the receiver operating characteristic curve (AUROC) of 0.89 and 0.92, 0.88 and 0.87, and 0.86 and 0.84 for 4 h, 8 h, and 12 h before sepsis onset, respectively. Conclusions: Using ICU data, sepsis onset can be predicted up to 12 h in advance. Our findings offer an early solution for mitigating the risk of sepsis onset. Highlights: We have developed Smart Sepsis Predictor (SSP) for sepsis prediction in patients admitted to intensive care units (ICUs). SSP is modeled using the 2019 PhysioNet/Computing in Cardiology Challenge dataset, which includes 40, 366 patients records. We use the Sepsis-3 criteria as the gold standard for predicting sepsis. Based on input types, SSP makes predictions in two modes using vital signs, demographics, and laboratory test results.