Publisher DOI: 10.22489/CinC.2019.297
Title: An Ensemble LSTM Architecture for Clinical Sepsis Detection
Language: English
Authors: Schellenberger, Sven 
Shi, Kilin 
Wiedemann, Jan Philipp 
Lurz, Fabian 
Weigel, Robert 
Kölpin, Alexander  
Issue Date: 1-Sep-2019
Source: Computing in Cardiology : 9005457 (2019-09-01)
Journal or Series Name: Computing in cardiology 
Abstract (english): 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".
ISBN: 978-172816936-1
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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