Forecasting the Effects of Real-Time Indoor PM2.5 on Peak Expiratory Flow Rates (PEFR) of Asthmatic Children in Korea: A Deep Learning Approach

Forecasting the Effects of Real-Time Indoor PM2.5 on Peak Expiratory Flow Rates (PEFR) of Asthmatic Children in Korea: A Deep Learning Approach

We built a deep learning algorithm to predict the deterioration of health symptoms among asthmatic children between 8–12 years of age. It is based on Peak Expiratory Flow Rates (PEFR) and indoor air pollution data, as well as meteorological data collected at their indoor residences every...

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Bibliographic Details
Main Authors: Jiyoung Woo, Ji-Hyun Lee, Yeonjin Kim, Guillaume Rudasingwa, Dae Hyun Lim, Sungroul Kim
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Asthma
big data
machine learning
recurrent neural network
peak expiratory flow rates (PEFR)
Online Access:https://ieeexplore.ieee.org/document/9703317/

Internet

https://ieeexplore.ieee.org/document/9703317/

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