Early sepsis detection
Being able to predict accurately whether sepsis will occur in a patient would dramatically improve patient outcomes. With the aim of predicting sepsis, this notebook showcases the use of path signatures as features for training a classifier on electronic health data. The data used to train the model in this notebook are the sequences of physiological and laboratory-observed measurements contained in the MIMIC-III dataset. These data include e.g. patients' heart rates, temperatures, and oxygen saturation levels, recorded repeatedly over time for each patient. The task is to use the classifier to predict whether a given patient will go on to develop sepsis, based on their measurement sequences recorded up to the time of prediction.