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A Study of Early Sepsis Detection Models Based on Multivariate Medical Time Series

Sepsis is a life-threatening complication caused by the body’s response to an infection. For that reason, it is important to have an accurate method to detect sepsis as early as possible. The features extracted from the used ICU data have missing values and non-uniform sampling frequencies, hence an advanced GP based interpolation method is proposed that increases the performance of the models. Additionally, this thesis abstract develops and compares different sepsis detection models based on real medical data [1]. The results show that accurate models can be developed to predict the occurrence of sepsis during an ICU stay.