Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children

Abstract The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient...

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Autores principales:	Bin Zhang, Xiang Wan, Fu-sheng Ouyang, Yu-hao Dong, De-hui Luo, Jing Liu, Long Liang, Wen-bo Chen, Xiao-ning Luo, Xiao-kai Mo, Lu Zhang, Wen-hui Huang, Shu-fang Pei, Bao-liang Guo, Chang-hong Liang, Zhou-yang Lian, Shui-xing Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad117bf7caa74d37a6f68d8b0d07eb2b
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spelling	oai:doaj.org-article:ad117bf7caa74d37a6f68d8b0d07eb2b2021-12-02T12:32:27ZMachine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children10.1038/s41598-017-05505-82045-2322https://doaj.org/article/ad117bf7caa74d37a6f68d8b0d07eb2b2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05505-8https://doaj.org/toc/2045-2322Abstract The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 109/L (RI: 49.47, p < 0.001) was the top predictor of severe HFMD, followed by spinal cord involvement (RI: 26.62, p < 0.001), spinal nerve roots involvement (RI: 10.34, p < 0.001), hyperglycemia (RI: 3.40, p < 0.001), and brain or spinal meninges involvement (RI: 2.45, p = 0.003). Interactions between elevated WBC count and hyperglycemia (H statistic: 0.231, 95% CI: 0–0.262, p = 0.031), between spinal cord involvement and duration of fever ≥3 days (H statistic: 0.291, 95% CI: 0.035–0.326, p = 0.035), and between brainstem involvement and body temperature (H statistic: 0.313, 95% CI: 0–0.273, p = 0.017) were observed. Therefore, GBT is capable to identify the predictors for severe HFMD and their interaction effects, outperforming conventional regression methods.Bin ZhangXiang WanFu-sheng OuyangYu-hao DongDe-hui LuoJing LiuLong LiangWen-bo ChenXiao-ning LuoXiao-kai MoLu ZhangWen-hui HuangShu-fang PeiBao-liang GuoChang-hong LiangZhou-yang LianShui-xing ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bin Zhang Xiang Wan Fu-sheng Ouyang Yu-hao Dong De-hui Luo Jing Liu Long Liang Wen-bo Chen Xiao-ning Luo Xiao-kai Mo Lu Zhang Wen-hui Huang Shu-fang Pei Bao-liang Guo Chang-hong Liang Zhou-yang Lian Shui-xing Zhang Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
description	Abstract The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 109/L (RI: 49.47, p < 0.001) was the top predictor of severe HFMD, followed by spinal cord involvement (RI: 26.62, p < 0.001), spinal nerve roots involvement (RI: 10.34, p < 0.001), hyperglycemia (RI: 3.40, p < 0.001), and brain or spinal meninges involvement (RI: 2.45, p = 0.003). Interactions between elevated WBC count and hyperglycemia (H statistic: 0.231, 95% CI: 0–0.262, p = 0.031), between spinal cord involvement and duration of fever ≥3 days (H statistic: 0.291, 95% CI: 0.035–0.326, p = 0.035), and between brainstem involvement and body temperature (H statistic: 0.313, 95% CI: 0–0.273, p = 0.017) were observed. Therefore, GBT is capable to identify the predictors for severe HFMD and their interaction effects, outperforming conventional regression methods.
format	article
author	Bin Zhang Xiang Wan Fu-sheng Ouyang Yu-hao Dong De-hui Luo Jing Liu Long Liang Wen-bo Chen Xiao-ning Luo Xiao-kai Mo Lu Zhang Wen-hui Huang Shu-fang Pei Bao-liang Guo Chang-hong Liang Zhou-yang Lian Shui-xing Zhang
author_facet	Bin Zhang Xiang Wan Fu-sheng Ouyang Yu-hao Dong De-hui Luo Jing Liu Long Liang Wen-bo Chen Xiao-ning Luo Xiao-kai Mo Lu Zhang Wen-hui Huang Shu-fang Pei Bao-liang Guo Chang-hong Liang Zhou-yang Lian Shui-xing Zhang
author_sort	Bin Zhang
title	Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
title_short	Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
title_full	Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
title_fullStr	Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
title_full_unstemmed	Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children
title_sort	machine learning algorithms for risk prediction of severe hand-foot-mouth disease in children
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/ad117bf7caa74d37a6f68d8b0d07eb2b
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Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children

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