VGTU talpykla > Elektronikos fakultetas / Faculty of Electronics > Moksliniai straipsniai / Research articles >

Lietuvių   English
Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/4025

Title: Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models
Authors: Abromavičius, Vytautas
Plonis, Darius
Tarasevičius, Deividas
Serackis, Artūras
Keywords: early detection
sepsis
evaluation metrics
machine learning
medical informatics
feature extraction
physionet challenge
Issue Date: 2020
Publisher: MDPI
Citation: Abromavičius, V.; Plonis, D.; Tarasevičius, D.; Serackis, A. Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models. Electronics 2020, 9, 1133.
Series/Report no.: 9;7
Abstract: The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a method taking into the account the amount of time the patients spent in the intensive care unit (ICU) was proposed. The proposed method uses two separate ensemble models, one trained on patient records under 56 h in the ICU, and another for patients who stayed longer than 56 h. A solution including feature selection and weighting based training on imbalanced data was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied.
Description: This article belongs to the Special Issue Computational Intelligence in Healthcare
URI: http://dspace.vgtu.lt/handle/1/4025
ISSN: 2079-9292
Appears in Collections:Moksliniai straipsniai / Research articles

Files in This Item:

File Description SizeFormat
Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models.pdf254.25 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback