Utilization of the signature method to identify the early onset of sepsis from multivariate physiological time series in critical care monitoring

This study focuses on the development and validation of a new signature-based regression model, augmented with a particular choice of the handcrafted features, to identify a patient ' s risk of sepsis based on physiologic data streams. The model makes a positive or negative prediction of sepsis for every time interval since admission to the ICU. Design(s): The data were sourced from the PhysioNet/Computing in Cardiology Challenge 2019 on the " Early Prediction of Sepsis from Clinical Data. " It consisted of ICU patient data from three separate hospital systems. Algorithms were scored against a specially designed utility function that rewards early predictions in the most clinically relevant region around sepsis onset and penalizes late predictions and false positives. Setting(s): The work was completed as part of the PhysioNet 2019 Challenge alongside 104 other teams. Patient(s): PhysioNet sourced over 60,000 ICU patients with up to 40 clinical variables for each hour of a patient ' s ICU stay. The Sepsis-3 criteria was used to define the onset of sepsis. Intervention(s): None. Measurements and Main Results: The algorithm yielded a utility function score which was the first placed entry in the official phase of the challenge.
Source: Current Awareness Service for Health (CASH) - Category: Consumer Health News Source Type: news