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Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 Computing in Cardiology, CinC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728169361
DOIs
Publication statusPublished - 1 Sep 2019
Publication typeA4 Article in a conference publication
Event2019 Computing in Cardiology, CinC 2019 - Singapore, Singapore
Duration: 8 Sep 201911 Sep 2019

Publication series

NameComputing in Cardiology
Volume2019-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference2019 Computing in Cardiology, CinC 2019
CountrySingapore
CitySingapore
Period8/09/1911/09/19

Abstract

Sepsis is caused by the dysregulated host response to infection and potentially is the main cause of 6 million death annually. It is a highly dynamic syndrome and therefore the early prediction of sepsis plays a key role in reducing its high associated mortality. However, this is a challenging task because there is no specific and accurate test or scoring system to perform early prediction. In this paper, we present a systematic approach for sepsis prediction. We also propose a new set of features to model the missingness in clinical data. The pipeline of the proposed method comprises three major components: feature extraction, feature selection, and classification. In total, 407 features are extracted from the clinical data. Then, five different sets of features are selected using a wrapper feature selection algorithm based on XGboost. The selected features are extracted from both valid and missing clinical data. Afterwards, an ensemble model consists of five XGboost models is used for sepsis prediction. The proposed algorithm is ranked officially as third place in the PhysioNet/Computing in Cardiology Challenge 2019 with an overall utility score of 0.339 on the unseen test dataset (our team name: Separatrix).