Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources
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Influenza‑like illness prediction using a long short‑term memory deep learning model with multiple open data sources Chao‑Tung Yang1 · Yuan‑An Chen1 · Yu‑Wei Chan2 · Chia‑Lin Lee3,4,5 · Yu‑Tse Tsan6,7 · Wei‑Cheng Chan6 · Po‑Yu Liu8
© The Author(s) 2020
Abstract The influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenzalike illness emergency department visits to the Taiwan Centers for Disease Control, and the P M2.5 open-source data from the Taiwan Environmental Protection Administration’s air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region. Keywords Influenza-like illness · LSTM · PM2.5 · Deep learning
1 Introduction Human physiological disorders reflect an altered condition that interferes with or modifies the vital functions of various organs or body parts [1]. Kim et al. [2] examined whether the severity of posttraumatic stress disorder (PTSD) symptoms and perceived functional impairment in firefighters with current possible PTSD are * Yu‑Tse Tsan [email protected] Extended author information available on the last page of the article
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correlated with the use of mental health services. Gautam et al. [3] also studied the treatment of medical conditions in humans, and found the rate of predictive accuracy obtained by ant colonyoptimization (ACO) and neural network hybridization to be more promising than other individual or hybrid approaches. Based on their predicted human health implications, Van der Fels-Klerx et al. [4] thoroughly reviewed criteria for rating risks related to food safety and dietary hazards. The method to be used should be selected based on the criteria of the risk manager/assessor, the quality of data, and the method’s characteristics. Influenza is a direct and far-reaching problem. An uncontrolled flu epidemic can have a significant impact on all of society, for example, in the global H1N1 influenza outbreak around the world in 2009. According to data released by the World Health Organization, there were more
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