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  4. An Ensemble LSTM Architecture for Clinical Sepsis Detection
 
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An Ensemble LSTM Architecture for Clinical Sepsis Detection

Publikationstyp
Journal Article
Date Issued
2019-09-01
Sprache
English
Author(s)
Schellenberger, Sven  orcid-logo
Shi, Kilin  
Wiedemann, Jan Philipp  
Lurz, Fabian  
Weigel, Robert  
Kölpin, Alexander  orcid-logo
TORE-URI
http://hdl.handle.net/11420/6458
Journal
Computing in cardiology  
Volume
2019-September
Article Number
9005457
Citation
Computing in Cardiology : 9005457 (2019-09-01)
Publisher DOI
10.22489/CinC.2019.297
Scopus ID
2-s2.0-85081114546
Sepsis is a life-threatening condition that has to be treated at an early stage. Doctors use the Sequential Organ Failure Assessment score for the earliest possible recognition. In addition, the practitioner's many years of experience help in order to facilitate an immediate response. Mortality decreases with every hour that sepsis is detected and treated with antibiotics. In this years PhysioNet/Computing in Cardiology Challenge the objective is to automatically detect sepsis six hours before the clinical prediction. This paper describes the implementation of an Long Short-Term Memory network for an early detection of sepsis in provided hourly physiological data. An utility score of 0.29 was achieved when testing on the full hidden test set. All entries were submitted using the team name "404: Sepsis not found".
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