TY - JOUR
T1 - Predicting asthma attacks
T2 - Effects of indoor PM concentrations on peak expiratory flow rates of asthmatic children
AU - Kim, Dohyeong
AU - Cho, Sunghwan
AU - Tamil, Lakshman
AU - Song, Dae Jin
AU - Seo, Sungchul
N1 - Funding Information:
This work was supported in part by the Environmental Health Action Program (Development of Receptor-Based Environment-Induced Diseases Prevention and Management System Using Real-Time Collected Environment and Health Information) under Project 2018001350005, and in part by the Research of Korea Centers for Disease Control and Prevention, South Korea, under Grant 2016ER670300.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as 'Green' (normal), 'Yellow' (mild to moderate exacerbation) and 'Red' (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making.
AB - Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as 'Green' (normal), 'Yellow' (mild to moderate exacerbation) and 'Red' (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making.
KW - Asthma
KW - deep learning
KW - indoor particulate matter
KW - peak expiratory flow rates
KW - real-time monitoring
UR - http://www.scopus.com/inward/record.url?scp=85078301060&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2960551
DO - 10.1109/ACCESS.2019.2960551
M3 - Article
AN - SCOPUS:85078301060
SN - 2169-3536
VL - 8
SP - 8791
EP - 8797
JO - IEEE Access
JF - IEEE Access
M1 - 8936445
ER -
