Applicability of internet search index for asthma admission forecast using machine learning. (15th April 2018)

• Record Type: Journal Article
• Authors: Luo, Li; Liao, Chengcheng; Zhang, Fengyi; Zhang, Wei; Li, Chunyang; Qiu, Zhixin; Huang, Debin
• Is Part Of: International journal of health planning and management. Volume 33:Number 3(2018)
• Page Start: 723
• Page End: 732
• Publication Date: 2018-04-15
• Subjects: asthma hospitalization -- internet search -- machine learning; Health planning -- Periodicals; Health services administration -- Periodicals; Santé publique -- Planification -- Périodiques; Santé, Services de -- Administration -- Périodiques; 362.1068
• DOI: 10.1002/hpm.2525
• Languages: English
• ISSNs: 0749-6753

Abstract

Summary: Objective: This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long‐term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance. Methods: In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning. Results: Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier. Conclusion: A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag‐effect to a certain extent. The addition of a real‐time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index.