Abstract
BackgroundSepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input.MethodsIn our study, we analyzed patients’ conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets “near” to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers.ResultsWe demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models.ConclusionApplying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.
Highlights
Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes
If we demonstrate that kinematics features (KF) increases the accuracy of early detection of sepsis, we will have contributed to our intended objectives
Rather than using traditional prediction models, we applied classification models that could discriminate if a set of time-series data represents a positive or negative patient for sepsis at a certain number of hours before sepsis onset (HBS)
Summary
Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. Sepsis is a fatal organic dysfunction caused by a patient’s deregulated response to infection, and septic shock is a subset of sepsis where circulatory and cellular/metabolic dysfunction occurs with a higher risk of mortality [1, 2]. The occurrence of sepsis and septic shock treated in a hospital is 437 and 270 cases per 100,000 person-years, respectively, with a Currently, several hospitals use the sepsis clinical score, called sequential organ failure assessment (SOFA), to define if a patient is diagnosed with sepsis. The score was recommended by the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) [1]. The authors proposed quickSOFA, or qSOFA, as the rapid score bedside criteria to facilitate the identification of patients with suspected infection and high risk of death [6].
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