Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction
Joint Authors
Singer, Pierre
Cohen, Jonathan
Bloch, Eli
Rotem, Tammy
Aperstein, Yehudit
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Objective.
Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU).
A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs.
Methodology.
We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay.
These features are used in machine learning algorithms to provide a prediction of a patient’s likelihood to develop sepsis during ICU stay, hours before it is diagnosed.
Results.
Five machine learning algorithms were implemented using R software packages.
The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs.
These algorithms aimed to calculate a patient’s probability to become septic within the next 4 hours, based on recordings from the last 8 hours.
The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%.
Conclusions.
The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs.
Variability of a patient’s vital signs proves to be a good indicator of one’s chance to become septic during ICU stay.
American Psychological Association (APA)
Bloch, Eli& Rotem, Tammy& Cohen, Jonathan& Singer, Pierre& Aperstein, Yehudit. 2019. Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175272
Modern Language Association (MLA)
Bloch, Eli…[et al.]. Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction. Journal of Healthcare Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1175272
American Medical Association (AMA)
Bloch, Eli& Rotem, Tammy& Cohen, Jonathan& Singer, Pierre& Aperstein, Yehudit. Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1175272
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175272