Cargando…

Machine Learning Models of Survival Prediction in Trauma Patients

Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients fro...

Descripción completa

Detalles Bibliográficos
Autores principales: Rau, Cheng-Shyuan, Wu, Shao-Chun, Chuang, Jung-Fang, Huang, Chun-Ying, Liu, Hang-Tsung, Chien, Peng-Chen, Hsieh, Ching-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616432/
https://www.ncbi.nlm.nih.gov/pubmed/31195670
http://dx.doi.org/10.3390/jcm8060799
_version_ 1783433506824650752
author Rau, Cheng-Shyuan
Wu, Shao-Chun
Chuang, Jung-Fang
Huang, Chun-Ying
Liu, Hang-Tsung
Chien, Peng-Chen
Hsieh, Ching-Hua
author_facet Rau, Cheng-Shyuan
Wu, Shao-Chun
Chuang, Jung-Fang
Huang, Chun-Ying
Liu, Hang-Tsung
Chien, Peng-Chen
Hsieh, Ching-Hua
author_sort Rau, Cheng-Shyuan
collection PubMed
description Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
format Online
Article
Text
id pubmed-6616432
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-66164322019-07-18 Machine Learning Models of Survival Prediction in Trauma Patients Rau, Cheng-Shyuan Wu, Shao-Chun Chuang, Jung-Fang Huang, Chun-Ying Liu, Hang-Tsung Chien, Peng-Chen Hsieh, Ching-Hua J Clin Med Article Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity. MDPI 2019-06-05 /pmc/articles/PMC6616432/ /pubmed/31195670 http://dx.doi.org/10.3390/jcm8060799 Text en © 2019 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
Rau, Cheng-Shyuan
Wu, Shao-Chun
Chuang, Jung-Fang
Huang, Chun-Ying
Liu, Hang-Tsung
Chien, Peng-Chen
Hsieh, Ching-Hua
Machine Learning Models of Survival Prediction in Trauma Patients
title Machine Learning Models of Survival Prediction in Trauma Patients
title_full Machine Learning Models of Survival Prediction in Trauma Patients
title_fullStr Machine Learning Models of Survival Prediction in Trauma Patients
title_full_unstemmed Machine Learning Models of Survival Prediction in Trauma Patients
title_short Machine Learning Models of Survival Prediction in Trauma Patients
title_sort machine learning models of survival prediction in trauma patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616432/
https://www.ncbi.nlm.nih.gov/pubmed/31195670
http://dx.doi.org/10.3390/jcm8060799
work_keys_str_mv AT rauchengshyuan machinelearningmodelsofsurvivalpredictionintraumapatients
AT wushaochun machinelearningmodelsofsurvivalpredictionintraumapatients
AT chuangjungfang machinelearningmodelsofsurvivalpredictionintraumapatients
AT huangchunying machinelearningmodelsofsurvivalpredictionintraumapatients
AT liuhangtsung machinelearningmodelsofsurvivalpredictionintraumapatients
AT chienpengchen machinelearningmodelsofsurvivalpredictionintraumapatients
AT hsiehchinghua machinelearningmodelsofsurvivalpredictionintraumapatients