Early Prediction of Sepsis Considering Early Warning Scoring Systems

Pardis Biglarbeigi, Donal McLaughlin, Khaled Rjoob, - Abdullah, Niamh McCallan, Alicja Jasinska-Piadlo, RR Bond, D Finlay, Kok Yew Ng, Alan Kennedy, James McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sepsis is a noted cause of mortality in hospitalised patients, particularly patients in the ICU. Early prediction of sepsis facilitates a better targeted therapy which in turn reduces patient mortality rates. This study developed a methodology to allow automatic prediction of sepsis 6 hours prior to its clinical presentation. For this purpose, four vital signs comprising of HR, SBP, Temperature and respiratory rate, along with laboratory results for Platelets, WBC, Glucose and Creatinine are scored
using Prehospital Early Sepsis Detection (PRESEP) and Sequential Organ Failure Assessment (SOFA) Early Warning Scoring (EWS) systems or screening tools and Systemic Inflammatory Response Syndrome (SIRS) criteria to allow under-sampling. The weighted scores obtained from the screening tools are also used to categorise patients into 4 groups with different probabilities of facing sepsis in ICU.
The hourly data of each group is then trained through a KNN classifier to detect sepsis hours. The ensemble of classifiers are used to predict sepsis in all available
dataset. The proposed model developed by UlsterTeam is trained on training setA and evaluated on training setB. The evaluation of the model on the training setB of the publically available dataset shows the Utility Score, accuracy, AUROC and AUPRC of the model are 0.27, 0.97, 0.71 and 0.07 respectively.
Original languageEnglish
Title of host publicationComputing in Cardiology 2019
DOIs
Publication statusPublished - Sep 2019

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