Alotaibi Mohammed, Alnajjar Fady, Alsayed Badr A, Alhmiedat Tareq, Marei Ashraf M, Bushnag Anas, Ali Luqman
Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia.
Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates.
J Multidiscip Healthc. 2023 Dec 5;16:3799-3811. doi: 10.2147/JMDH.S435492. eCollection 2023.
Chronic lung-related diseases, with asthma being the most prominent example, characterized by diverse symptoms and triggers, present significant challenges in disease management and prediction of exacerbations across patients. This research aimed to devise a practical solution by introducing a personalized alert system tailored to individual lung function and environmental conditions, offering a holistic approach for the management of a range of chronic respiratory conditions.
In response to these challenges, we developed a personalized alert system based on individual lung function tests conducted in diverse environmental conditions, as determined by air-quality sensors. Our research was substantiated through an observational pilot study involving twelve healthy participants. These participants were exposed to varying air quality, temperature, and humidity conditions, and their lung function, as indicated by peak expiratory flow (PEF) values, was monitored.
The study revealed pronounced variability in pulmonary responses across different environments. Leveraging these findings, we proposed a design of a personalized alarm system that monitors air quality in real-time and issues alerts under potentially unfavorable environmental conditions. Additionally, we investigated the use of basic machine learning techniques to predict PEF values in these varied environmental settings.
The proposed system offers a proactive approach for individuals, particularly those with asthma, to actively manage their respiratory health. By providing real-time monitoring and personalized alerts, it aims to minimize exposure to potential asthma triggers. Ultimately, our system seeks to empower individuals with the tools for timely intervention, potentially reducing discomfort and enhancing management of asthma symptoms.
慢性肺部相关疾病，其中哮喘最为突出，其症状和触发因素多样，给患者的疾病管理和病情加重预测带来了重大挑战。本研究旨在通过引入一种根据个体肺功能和环境条件量身定制的个性化警报系统，设计出一种切实可行的解决方案，为一系列慢性呼吸道疾病的管理提供一种全面的方法。
针对这些挑战，我们基于在不同环境条件下通过空气质量传感器确定的个体肺功能测试，开发了一种个性化警报系统。我们的研究通过一项涉及12名健康参与者的观察性试点研究得到了证实。这些参与者暴露于不同的空气质量、温度和湿度条件下，并监测他们的肺功能，以呼气峰值流速（PEF）值表示。
研究揭示了不同环境下肺部反应的显著差异。利用这些发现，我们提出了一种个性化警报系统的设计，该系统可实时监测空气质量，并在潜在不利的环境条件下发出警报。此外，我们研究了使用基本机器学习技术在这些不同环境设置中预测PEF值。
所提出的系统为个人，特别是哮喘患者，提供了一种积极主动的方法来积极管理他们的呼吸健康。通过提供实时监测和个性化警报，它旨在尽量减少接触潜在的哮喘触发因素。最终，我们的系统旨在为个人提供及时干预的工具，有可能减少不适并加强哮喘症状的管理。
