Early Prediction of Sepsis Considering Early Warning Scoring Systems

P. Biglarbeigi, A. Kennedy, J. McLaughlin, D. McLaughlin, K. Rjoob, Abdullah Abdullah, N. McCallan, A. Jasinska-Piadlo, R. Bond, D. Finlay, K. Y. Ng

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

3 Citations (Scopus)


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 publication2019 Computing in Cardiology (CinC)
Number of pages4
ISBN (Electronic)9781728169361
ISBN (Print)9781728159423
Publication statusPublished - 24 Feb 2020
EventCinC 2019: Computing in Cardiology - Singapore, Singapore
Duration: 8 Sept 201911 Sept 2019

Publication series

ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceCinC 2019


  • Tools
  • Training
  • Sugar
  • Blood platelets
  • Input variables
  • Cardiology

ASJC Scopus subject areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


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