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AuthorZabihi M.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
Available date2022-04-26T12:31:21Z
Publication Date2019
Publication NameComputing in Cardiology
ResourceScopus
Identifierhttp://dx.doi.org/10.23919/CinC49843.2019.9005564
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081132099&doi=10.23919%2fCinC49843.2019.9005564&partnerID=40&md5=116b0f4e3bb5b51212e4bda62bae1ae4
URIhttp://hdl.handle.net/10576/30619
AbstractSepsis 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).
Languageen
PublisherIEEE Computer Society
SubjectCardiology
Data mining
Feature extraction
Forecasting
Intensive care units
Statistical tests
Clinical data
Early prediction
Ensemble modeling
Feature selection algorithm
Host response
Scoring systems
Sets of features
Third places
Classification (of information)
TitleSepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
TypeConference Paper
Volume Number2019-September
dc.accessType Abstract Only


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