Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/CinC49843.2019.9005630 | 2019 Computing in Cardiology (CinC) |
Keywords | DocType | ISSN |
screening tools,systemic inflammatory response syndrome criteria,weighted scores,sepsis hours,Utility Score,hospitalised patients,patient mortality rates,automatic prediction,early warning scoring systems,targeted therapy,respiratory rate,platelets,glucose,creatinine,prehospital early sepsis detection,sequential organ failure assessment,probability,KNN classifier,systolic blood pressure,heart rate,WBC,time 6.0 hour | Conference | 2325-8861 |
ISBN | Citations | PageRank |
978-1-7281-5942-3 | 0 | 0.34 |
References | Authors | |
2 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pardis Biglarbeigi | 1 | 1 | 2.03 |
| Donal McLaughlin | 2 | 0 | 0.34 |
| Khaleed Rjoob | 3 | 0 | 0.34 |
| Abdullah Abdullah | 4 | 0 | 0.34 |
| Niamh McCallan | 5 | 0 | 0.34 |
| Alicja Jasinska-Piadlo | 6 | 0 | 0.34 |
| RR Bond | 7 | 68 | 30.02 |
| Dewar D. Finlay | 8 | 87 | 22.60 |
| Mark Kok Yew Ng | 9 | 0 | 0.34 |
| Alan Kennedy | 10 | 0 | 0.34 |
| McLaughlin, J. | 11 | 4 | 11.37 |