Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

Abstract

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. INDEX TERMS Asthma, indoor particulate matter, deep learning, peak expiratory flow rates, real-time monitoring.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data

“…Most of the previous studies still exhibit unsatisfactory accuracy for predicting the risk of asthma exacerbation in individual patients. A recent related work, proposed by Kim et al [ 32 ], utilizes a deep learning model to predict asthma exacerbations among pediatric asthma patients with 70% precision for the prediction of the risk zone based on the EU asthma zoning scale [ 21 ]. Our work differentiates itself by utilizing an individualized-level risk zoning which is more accurate in comparison to public-level risk zoning as it is based on patients’ accumulated data.…”

Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges

“…Aside from the efforts to finetune the existing mobile sensors and devices, dedicated efforts should be made to develop GPS-enabled wearable and patchable devices as a next-generation environmental and health monitoring tool that assesses the true level of environmental exposure to the human body at every second or minute interval [41,42]. The recent development of IoT technology and deep learning algorithm could serve as promising tool for processing and analyzing the real-time air pollution data [43,44] that underpins the backbone of evidence-based personalized medicine and environmental exposure management.…”

Building an indoor air quality monitoring system based on the architecture of the Internet of Things

“…29 Effects of fine particulate matters on AQI values. MATLAB was also used to simulate the effects of fine particulate matters and CO in the environment on the air quality sub-indices CO 2 or fine particulate matters exceed the standard, the Arduino Uno board will activate the ventilation unit and air purifier through the infrared emitter to reduce the concentration [26][27][28].…”