Cargando…
Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression
Sepsis-induced coagulopathy has poor prognosis; however, there is no established tool for predicting it. We aimed to create predictive models for coagulopathy progression using machine-learning techniques to evaluate predictive accuracies of machine-learning and conventional techniques. A post-hoc s...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408668/ https://www.ncbi.nlm.nih.gov/pubmed/32635574 http://dx.doi.org/10.3390/jcm9072113 |
_version_ | 1783567884571639808 |
---|---|
author | Hasegawa, Daisuke Yamakawa, Kazuma Nishida, Kazuki Okada, Naoki Murao, Shuhei Nishida, Osamu |
author_facet | Hasegawa, Daisuke Yamakawa, Kazuma Nishida, Kazuki Okada, Naoki Murao, Shuhei Nishida, Osamu |
author_sort | Hasegawa, Daisuke |
collection | PubMed |
description | Sepsis-induced coagulopathy has poor prognosis; however, there is no established tool for predicting it. We aimed to create predictive models for coagulopathy progression using machine-learning techniques to evaluate predictive accuracies of machine-learning and conventional techniques. A post-hoc subgroup analysis was conducted based on the Japan Septic Disseminated Intravascular Coagulation retrospective study. We used the International Society on Thrombosis and Haemostasis disseminated intravascular coagulation (DIC) score to calculate the ΔDIC score as ((DIC score on Day 3) − (DIC score on Day 1)). The primary outcome was to determine whether the predictive accuracy of ΔDIC was more than 0. The secondary outcome was the actual predictive accuracy of ΔDIC (predicted ΔDIC−real ΔDIC). We used the machine-learning methods, such as random forests (RF), support vector machines (SVM), and neural networks (NN); their predictive accuracies were compared with those of conventional methods. In total, 1017 patients were included. Regarding DIC progression, predictive accuracy of the multiple linear regression, RF, SVM, and NN models was 63.7%, 67.0%, 64.4%, and 59.8%, respectively. The difference between predicted ΔDIC and real ΔDIC was 2.05, 1.54, 2.24, and 1.77 for the multiple linear regression, RF, SVM, and NN models, respectively. RF had the highest predictive accuracy. |
format | Online Article Text |
id | pubmed-7408668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74086682020-08-13 Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression Hasegawa, Daisuke Yamakawa, Kazuma Nishida, Kazuki Okada, Naoki Murao, Shuhei Nishida, Osamu J Clin Med Article Sepsis-induced coagulopathy has poor prognosis; however, there is no established tool for predicting it. We aimed to create predictive models for coagulopathy progression using machine-learning techniques to evaluate predictive accuracies of machine-learning and conventional techniques. A post-hoc subgroup analysis was conducted based on the Japan Septic Disseminated Intravascular Coagulation retrospective study. We used the International Society on Thrombosis and Haemostasis disseminated intravascular coagulation (DIC) score to calculate the ΔDIC score as ((DIC score on Day 3) − (DIC score on Day 1)). The primary outcome was to determine whether the predictive accuracy of ΔDIC was more than 0. The secondary outcome was the actual predictive accuracy of ΔDIC (predicted ΔDIC−real ΔDIC). We used the machine-learning methods, such as random forests (RF), support vector machines (SVM), and neural networks (NN); their predictive accuracies were compared with those of conventional methods. In total, 1017 patients were included. Regarding DIC progression, predictive accuracy of the multiple linear regression, RF, SVM, and NN models was 63.7%, 67.0%, 64.4%, and 59.8%, respectively. The difference between predicted ΔDIC and real ΔDIC was 2.05, 1.54, 2.24, and 1.77 for the multiple linear regression, RF, SVM, and NN models, respectively. RF had the highest predictive accuracy. MDPI 2020-07-04 /pmc/articles/PMC7408668/ /pubmed/32635574 http://dx.doi.org/10.3390/jcm9072113 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hasegawa, Daisuke Yamakawa, Kazuma Nishida, Kazuki Okada, Naoki Murao, Shuhei Nishida, Osamu Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title | Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title_full | Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title_fullStr | Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title_full_unstemmed | Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title_short | Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression |
title_sort | comparative analysis of three machine-learning techniques and conventional techniques for predicting sepsis-induced coagulopathy progression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408668/ https://www.ncbi.nlm.nih.gov/pubmed/32635574 http://dx.doi.org/10.3390/jcm9072113 |
work_keys_str_mv | AT hasegawadaisuke comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression AT yamakawakazuma comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression AT nishidakazuki comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression AT okadanaoki comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression AT muraoshuhei comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression AT nishidaosamu comparativeanalysisofthreemachinelearningtechniquesandconventionaltechniquesforpredictingsepsisinducedcoagulopathyprogression |