Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients

Abstract Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of thi...

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Autores principales:	Hua Liu, Hua Yuan, Yongmei Wang, Weiwei Huang, Hui Xue, Xiuying Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8de473c4764541f7a90d60bce7ebd37b
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spelling	oai:doaj.org-article:8de473c4764541f7a90d60bce7ebd37b2021-12-02T17:24:00ZPrediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients10.1038/s41598-021-92287-92045-2322https://doaj.org/article/8de473c4764541f7a90d60bce7ebd37b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92287-9https://doaj.org/toc/2045-2322Abstract Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.Hua LiuHua YuanYongmei WangWeiwei HuangHui XueXiuying ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hua Liu Hua Yuan Yongmei Wang Weiwei Huang Hui Xue Xiuying Zhang Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
description	Abstract Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.
format	article
author	Hua Liu Hua Yuan Yongmei Wang Weiwei Huang Hui Xue Xiuying Zhang
author_facet	Hua Liu Hua Yuan Yongmei Wang Weiwei Huang Hui Xue Xiuying Zhang
author_sort	Hua Liu
title	Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
title_short	Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
title_full	Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
title_fullStr	Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
title_full_unstemmed	Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
title_sort	prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8de473c4764541f7a90d60bce7ebd37b
work_keys_str_mv	AT hualiu predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients AT huayuan predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients AT yongmeiwang predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients AT weiweihuang predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients AT huixue predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients AT xiuyingzhang predictionofvenousthromboembolismwithmachinelearningtechniquesinyoungmiddleagedinpatients
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Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients

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