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Luo L, Liao C, Zhang F, et al. Applicability of internet search index for asthma admission forecast using machine learning. Int J Health Plann Manage. 2018;doi: 10.1002/hpm.2525.
Luo, L., Liao, C., Zhang, F., Zhang, W., Li, C., Qiu, Z., & Huang, D. (2018). Applicability of internet search index for asthma admission forecast using machine learning. The International journal of health planning and management, . https://doi.org/10.1002/hpm.2525
Luo, Li, et al. "Applicability of internet search index for asthma admission forecast using machine learning." The International journal of health planning and management vol. (2018). doi: https://doi.org/10.1002/hpm.2525
Luo L, Liao C, Zhang F, Zhang W, Li C, Qiu Z, Huang D. Applicability of internet search index for asthma admission forecast using machine learning. Int J Health Plann Manage. 2018 Apr 15; doi: 10.1002/hpm.2525. Epub 2018 Apr 15. PMID: 29656461.
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Int J Health Plann Manage. 2018 Apr 15; doi: 10.1002/hpm.2525. Epub 2018 Apr 15.

Applicability of internet search index for asthma admission forecast using machine learning.

The International journal of health planning and management

Li Luo, Chengcheng Liao, Fengyi Zhang, Wei Zhang, Chunyang Li, Zhixin Qiu, Debin Huang

Affiliations

  1. Business School, Sichuan University, China.
  2. West China Biomedical Big Data Center, West China Hospital, Sichuan University, China.
  3. Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, China.
  4. Chengdu Medical Insurance Administration, China.

PMID: 29656461 DOI: 10.1002/hpm.2525

Abstract

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.

Copyright © 2018 John Wiley & Sons, Ltd.

Keywords: asthma hospitalization; internet search; machine learning

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