LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock

Abstract

Sepsis is a major health concern with global estimates of 31.5 million cases per year. Case fatality rates are still unacceptably high, and early detection and treatment is vital since it significantly reduces mortality rates for this condition. Appropriately designed automated detection tools have the potential to reduce the morbidity and mortality of sepsis by providing early and accurate identification of patients who are at risk of developing sepsis. In this paper, we present “LiSep LSTM”; a Long Short-Term Memory neural network designed for early identification of septic shock. LSTM networks are typically well-suited for detecting long-term dependencies in time series data. LiSep LSTM was developed using the machine learning framework Keras with a Google TensorFlow back end. The model was trained with data from the Medical Information Mart for Intensive Care database which contains vital signs, laboratory data, and journal entries from approximately 59,000 ICU patients. We show that LiSep LSTM can outperform a less complex model, using the same features and targets, with an AUROC 0.8306 (95% confidence interval: 0.8236, 0.8376) and median offsets between prediction and septic shock onset up to 40 hours (interquartile range, 20 to 135 hours). Moreover, we discuss how our classifier performs at specific offsets before septic shock onset, and compare it with five state-of-the-art machine learning algorithms for early detection of sepsis.

Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach

“…The limitations of existing scoring systems have lead to a rise in researchers exploring machine learning techniques for mortality prediction [13][14][15][16][17][18][19][20] , as well as the related issues of predicting the onset of various intervention methods 21,22 detecting the risk of sepsis [23][24][25][26] and other clinical deterioration events 27,28 . Machine learning approaches have the advantage of being relatively easy to continuously update and recalibrate, with algorithms OPEN 1 College of Science and Engineering, James Cook University, Townsville 4811, Australia.…”

Long Short-Term Memory Recurrent Neural Network for Tidal Level Forecasting

“…LSTM networks are a variant of RNNs that have been applied in fields such as biomedical science [25], speech recognition [26], sentiment analysis [27], and image classification [28]. However, LSTM recurrent neural networks have not yet been applied in tidal water level forecasting.…”

Antimicrobial stewardship programs; a two-part narrative review of step-wise design and issues of controversy. Part II: Ten questions reflecting knowledge gaps and issues of controversy in the field of antimicrobial stewardship

“…Is there a conflict between optimising awareness and empirical therapy for acute, potentially severe infections (most notably sepsis) and the objectives of ASPs? Development of machine-learning algorithms with great potential to improve early identification of sepsis are underway, 28 but as long as widely used clinical algorithms for early identification of potentially severe infections, such as sepsis, have limited sensitivity and specificity, a tension between the objective of early identification and treatment of potentially severe infections and limiting over-use of antibiotics may be difficult to avoid entirely. 29 The ambition to increase awareness of sepsis and the ambition to promote antimicrobial stewardship both strive to improve healthcare, but are not discussed together often enough as integrated parts of infectious disease care.…”