Predicting Peak Demand Days for Asthma-Related Emergency Hospitalisations: A Machine Learning Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7855-7_1
Reference12 articles.
1. Accordini, S., et al.: The cost of persistent asthma in Europe: an international population-based study in adults. Int. Arch. Allergy Immunol. 160, 93–101 (2012). https://doi.org/10.1159/000338998
2. Babin, S., et al.: Pediatric patient asthma-related emergency department visits and admissions in Washington, DC, from 2001–2004, and associations with air quality, socio-economic status and age group. Environ. Health Glob. Access Sci. Source 6, 9 (2007). https://doi.org/10.1186/1476-069X-6-9
3. Bradley, S.: How common is asthma? Worldwide facts and statistics (2022). https://www.singlecare.com/blog/asthma-statistics/. Accessed 09 April 2022
4. Bridge, J., Blakey, J.D., Bonnett, L.J.: A systematic review of methodology used in the development of prediction models for future asthma exacerbation. BMC Med. Res. Methodol. 20 (2020)
5. Buyuktiryaki, B., et al.: Predicting hospitalization in children with acute asthma. J. Emergency Med. 44 (2013). https://doi.org/10.1016/j.jemermed.2012.10.015
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