Using machine learning to examine the relationship between asthma and absenteeism
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...
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| Veröffentlicht in: | Environmental monitoring and assessment 2019-06, Vol.191 (Suppl 2), p.332-9, Article 332 |
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| creator | Lary, Maria-Anna Allsopp, Leslie Lary, David J. Sterling, David A. |
| description | 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. |
| doi_str_mv | 10.1007/s10661-019-7423-2 |
| format | Article |
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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.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-019-7423-2</identifier><identifier>PMID: 31254081</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Absenteeism ; Academic Success ; Adolescent ; Artificial intelligence ; Asthma ; Asthma - epidemiology ; Atmospheric Protection/Air Quality Control/Air Pollution ; Child ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Environment ; Environmental Health - methods ; Environmental Health - statistics & numerical data ; Environmental Management ; Environmental monitoring ; Environmental science ; Estimation ; Female ; Humans ; Learning algorithms ; Machine Learning ; Male ; Monitoring/Environmental Analysis ; Public health ; Schools ; Students ; Texas - epidemiology ; Topical Collection on Geospatial Technology in Environmental Health Applications</subject><ispartof>Environmental monitoring and assessment, 2019-06, Vol.191 (Suppl 2), p.332-9, Article 332</ispartof><rights>Springer Nature Switzerland AG 2019</rights><rights>Environmental Monitoring and Assessment is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-9fdedbb67d0dddc4c37891d75df882252a051aec7bac6611e1becbe13b4804413</citedby><cites>FETCH-LOGICAL-c372t-9fdedbb67d0dddc4c37891d75df882252a051aec7bac6611e1becbe13b4804413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-019-7423-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-019-7423-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31254081$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lary, Maria-Anna</creatorcontrib><creatorcontrib>Allsopp, Leslie</creatorcontrib><creatorcontrib>Lary, David J.</creatorcontrib><creatorcontrib>Sterling, David A.</creatorcontrib><title>Using machine learning to examine the relationship between asthma and absenteeism</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>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.</description><subject>Absenteeism</subject><subject>Academic Success</subject><subject>Adolescent</subject><subject>Artificial intelligence</subject><subject>Asthma</subject><subject>Asthma - epidemiology</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Child</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Health - methods</subject><subject>Environmental Health - statistics & numerical data</subject><subject>Environmental Management</subject><subject>Environmental monitoring</subject><subject>Environmental science</subject><subject>Estimation</subject><subject>Female</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Monitoring/Environmental Analysis</subject><subject>Public health</subject><subject>Schools</subject><subject>Students</subject><subject>Texas - 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| ispartof | Environmental monitoring and assessment, 2019-06, Vol.191 (Suppl 2), p.332-9, Article 332 |
| issn | 0167-6369 1573-2959 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Absenteeism Academic Success Adolescent Artificial intelligence Asthma Asthma - epidemiology Atmospheric Protection/Air Quality Control/Air Pollution Child Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Health - methods Environmental Health - statistics & numerical data Environmental Management Environmental monitoring Environmental science Estimation Female Humans Learning algorithms Machine Learning Male Monitoring/Environmental Analysis Public health Schools Students Texas - epidemiology Topical Collection on Geospatial Technology in Environmental Health Applications |
| title | Using machine learning to examine the relationship between asthma and absenteeism |
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