Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Dec 12, 2017)
Date Submitted: Dec 11, 2017
Open Peer Review Period: Dec 12, 2017 - Dec 12, 2017
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Analysis of Internet Search Index on Asthma Admission Forecast based on Machine Learning
Background:
Internet Search Index is a powerful toll to both monitor and predict epidemic outbreaks. However, whether Internet Search Index can significantly improve asthma admissions forecast remains unknown.
Objective:
This study aimed to determine whether Search Index could provide insight into trends in asthma admission in China. And the long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma healthcare resource shortage in advance.
Methods:
In this paper, we creatively used Search Index combined with air pollution, weather data and historical admissions data to forecast asthma admissions based on machine learning.
Results:
Results showed that the best AUC in test set can achieve 0.832 with all predictors mentioned above.
Conclusions:
We can draw a conclusion that Search Index is a powerful predictor in asthma admissions forecast, and recent Search Index can reflect today’s asthma admissions with lag-effect to some extents. The addition of real-time, easily accessible Search Index improves the forecasting capabilities and demonstrates the predictive potential of Search Index.
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