Using machine learning to examine the relationship between asthma and absenteeism

Author:

Lary Maria-Anna,Allsopp Leslie,Lary David J.,Sterling David A.

Funder

Glazow Smith Kline

Boehringer Inngleheim

UNT Health Science Center for Professional and Continuing Education

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Reference26 articles.

1. Aguiar, E., Lakkaraju, H., Bhanpuri, N., Miller, D., Yuhas, B., Addison, K.L. (2015). Who, when, and why: A machine learning approach to prioritizing students at risk of not graduating high school on time. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 93–102): ACM.

2. Akinbami, L.J., Moorman, J.E., Garbe, P.L., Sondik, E.J. (2009). Status of childhood asthma in the united states, 1980–2007. Pediatrics, 123(Supplement 3), S131–S145.

3. Allensworth, E.M., Gwynne, J.A., Moore, P., de la Torre, M. (2014). Looking forward to high school and college: Middle grade indicators of readiness in chicago public schools. University of Chicago Consortium on Chicago School Research.

4. Balfanz, R., Herzog, L., Mac Iver, D.J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223–235.

5. Balfanz, R., & Byrnes, V. (2012). The importance of being in school: a report on absenteeism in the nation’s public schools. Baltimore: Johns Hopkins University Center for Social Organization of Schools.

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