Using machine learning to examine the relationship between asthma and absenteeism

被引：7

作者：

Lary, Maria-Anna ^{[3
]}

Allsopp, Leslie ^{[1
]}

Lary, David J. ^{[2
]}

Sterling, David A. ^{[1
]}

机构：

[1] Univ North Texas, Hlth Sci Ctr, Dept Biostat & Epidemiol, Sch Publ Hlth, Ft Worth, TX 76107 USA

[2] Univ Texas Dallas, William B Hanson Ctr Space Sci, 800 W Campbell Rd WT-15, Richardson, TX 75080 USA

[3] Univ North Texas, Hlth Sci Ctr, Sch Publ Hlth, Dept Biostat & Epidemiol, Ft Worth, TX 75082 USA

来源：

ENVIRONMENTAL MONITORING AND ASSESSMENT | 2019年 / 191卷 / Suppl 2期

关键词：

Asthma; Learning outcomes; Absenteeism; Machine learning; Environmental & Public Health; EDUCATIONAL-ATTAINMENT; ACADEMIC-ACHIEVEMENT; UNITED-STATES; HEALTH; SCHOOL;

D O I：

10.1007/s10661-019-7423-2

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this study, we found that machine learning was able to effectively estimate student learning outcomes geo-spatially across all the campuses in a large, urban, independent school district. The machine learning showed that key factors in estimating the student learning outcomes included the number of days students were absent from school. In turn, one of the most important factors in estimating the number of days a student was absent was whether or not the student had asthma. This highlights the importance of environmental public health for student learning outcomes.

引用

页数：9

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