Dataset Information

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 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?

SUBMITTER: Zhang B

PROVIDER: S-EPMC5511270 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zhang Bin B Wan Xiang X Ouyang Fu-Sheng FS Dong Yu-Hao YH Luo De-Hui DH Liu Jing J Liang Long L Chen Wen-Bo WB Luo Xiao-Ning XN Mo Xiao-Kai XK Zhang Lu L Huang Wen-Hui WH Pei Shu-Fang SF Guo Bao-Liang BL Liang Chang-Hong CH Lian Zhou-Yang ZY Zhang Shui-Xing SX

Scientific reports 20170714 1

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 × 10<sup>9</sup>/L (RI: ...[more]

PMID: 28710409

Similar Datasets

Project description:BackgroundSevere hand-foot-and-mouth disease (HFMD) is a life-threatening contagious disease among young children and infants. Although enterovirus A71 has been well acknowledged to be the dominant cause of severe HFMD, there still remain other unidentified risk factors for severe HFMD. Previous studies mainly focused on identifying the individual-level risk factors from a clinical perspective, while rare studies aimed to clarify the association between regional-level risk factors and severe HFMD, which may be more important from a public health perspective.MethodsWe retrieved the clinical HFMD counts between 2008 and 2014 from the Chinese Center for Disease Control and Prevention, which were used to calculated the case-severity rate in 143 prefectural-level cities in mainland China. For each of those 143 cities, we further obtained city-specific characteristics from the China City Statistical Yearbook (social and economic variables) and the national meteorological monitoring system (meteorological variables). A Poisson regression model was then used to estimate the associations between city-specific characteristics (reduced by the principal component analysis to avoid multicollinearity) and the case-severity rate of HFMD. The above analysis was further stratified by age and gender to examine potential modifying effects and vulnerable sub-populations.ResultsWe found that the case-severity rate of HFMD varied dramatically between cities, ranging from 0 to 8.09%. Cities with high case-severity rates were mainly clustered in Central China. By relating the case-severity rate to city-specific characteristics, we found that both the principal component characterized by a high level of social and economic development (RR = 0.823, 95%CI 0.739, 0.916) and another that characterized by warm and humid climate (RR = 0.771, 95%CI 0.619, 0.960) were negatively associated with the case-severity rate of HFMD. These estimations were consistent across age and gender sub-populations.ConclusionExcept for the type of infected pathogen, the case-severity rate of HFMD was closely related to city development and meteorological factor. These findings suggest that social and environmental factors may also play an important role in the progress of severe HFMD.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets