Ele-Monitoring Systems and Ontology-Based Models in Asthma Domain: History
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Contributor:
Hicham Ajami
,
Hamid Mcheick
,
Catherine Laprise

Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. 

  • asthma
  • SWRL
  • medical rules
  • pervasive computing
  • ontology model
  • architectural model

1. Introduction

Asthma is a chronic respiratory disease in which the lining of the lung airways becomes swollen and narrowed thus reducing the airflow into and out of the lung. This airflow obstruction can cause symptoms such as coughing, wheezing, shortness of breath, and chest tightness. The severity and the frequency of asthma exacerbations differ from one patient to another [1][2]. Asthma usually appears during childhood; however, some adults can also develop asthma [3]. Estimations show that asthma affects more than 339 million people worldwide [2]. In Canada, more than 3.8 million individuals [4], who are over one year of age, are asthmatics. Asthma can affect the quality of life of an asthma patient as well as his/her work productivity and psychological health [1]. There are two phenotypes of asthma: extrinsic or allergic asthma and intrinsic or non-allergic asthma. Allergic asthma is triggered after the inhalation of specific allergens: it usually develops early in life and is usually accompanied or preceded by atopic diseases such as allergic rhinitis, a food allergy, and atopic eczema [5][6]. Atopy is the genetic susceptibility of an allergic reaction being provoked after an exposure to an allergen [7]. On the other hand, in non-allergic asthma, no exogenous allergens can be identified, and this phenotype is characterized by a late onset. Allergic asthma is considered to be more responsive to treatment than non-allergic asthma, and it is the most common form of the disease [8]. An asthmatic response is provoked by an allergy in 75–80% of all asthmatic cases [9][10][11]. Asthma exacerbations are often triggered upon the exposure to environmental allergens or irritants (e.g., indoor and outdoor allergens and pollution), tobacco smoke, workplace irritants (e.g., food derivatives, fumes, chemical products, animal products, etc.), changes in the weather conditions or viral respiratory infections [1][2][6][7][9].
Over the past decades, asthma problems have been of particular interest in bioinformatics projects. However, asthma is a complex disease where multiple aspects need to be tackled to control its complications. Each asthma patient experiences such conditions uniquely; therefore, dynamic protection standards must be developed for each patient. Asthma patients need constant care to protect them from various environmental irritants and allergens. For example, dust mites, animal dander, humidity, extreme temperature, precipitation, some weed pollens and molds, and air pollutants [2][7][9], such as particulate matter PM2.5, PM10, O3, NO2, SO2, and CO, pose potentially serious and life-threatening risks to asthma patients. On the other hand, food allergies can increase the bronchial hyperreactivity in asthmatic patients as a part of the anaphylactic reaction. Although a food allergy is triggered after the ingestion of certain food derivatives, inhaled airborne allergens such as flour, egg allergens, nuts, soybean, tea dust, fish, and seafood may induce asthmatic reactions [6].

2. Ele-Monitoring Systems and Ontology-Based Models

The domain of digital health has grown rapidly over the last ten years. Healthcare projects are moving towards tele-monitoring systems, patients’ self-management, and computer-mediated counselling. The reports about chronic diseases have shown the need for a variety of effective solutions to improve the lives of patients. Context awareness and personalization are the most important elements of the success of cognitive computing in the modern healthcare domain. Context awareness refers to the use of external information that can influence a person’s situation, while personalization provides a tailored treatment approach to address each patient’s condition. Personalized context awareness can be realized using an ontological model for abstraction. The ontology that could be defined as a formal description of knowledge is one of the common methods to deal with the challenges of designing, managing, and integrating patients’ health data from heterogeneous sources to extract and infer useful information. Cognitive computing is currently being used in several asthma projects, for instance, Quinde et al. [11] presented an approach to develop context-aware systems aiding the individualized management of asthma. This work strives to shed light on the existing gaps of using context awareness in asthma management and determine the functionalities of such a context-aware system. Al-dowaihi et al. [12] propose an asthma prototype system that allows patients to self-supervise and manage their symptoms and conditions accurately in air-polluted areas, as well as informing their healthcare providers in critical cases.
Kwan et al. [13] developed a portable external mobile device accessory to collect PEF, FEV1, FEV6, NO, CO, and O2 from patients. The authors developed an application to record this information and send the results to a physician to track asthma symptoms and lung function in real time. According to the authors, this work would allow the physician to make an appropriate intervention in a patient’s medication regimen more quickly. Anantharam et al. [14] created a system called kHealth to aggregate multisensory and multimodal data from asthmatic patients using a combination of active and passive sensors. The project presents an advanced data analysis platform that can help physicians determine more precisely the cause and severity of asthma and therefore improve the quality of life of patients. Ra et al. [15] proposed a cloud-based system called AsthmaGuide, in which a smartphone is used as a center point for gathering information in real-time processes. The data are then uploaded to a cloud web application for both patients and doctors to receive advice and alarms. AsthmaGuide allows asthma patients to be involved in their care and treatment and allows healthcare professionals to provide more effective support. Dieffenderfer et al. [16] developed a wearable sensor system that enables us to measure the correlation between individual environmental exposures and physiological markers and subsequent adverse reactions. This framework will grant us understanding of the impact of increased ozone levels and other pollutants on asthma. Gyrard et al. [17] developed a personalized healthcare system for chronic diseases such as obesity and asthma; this system aggregates knowledge from different sources such as internet of things devices, clinical documentation, and electronic health records. Quinde et al. [18] proposed a new context-based approach to control asthma; this solution follows personalized management to address the heterogeneity of asthma. In addition, Galante et al. presented an approach to self-monitoring guidance (see Asthma management resources for healthcare) but a dynamic aspect is not implemented [19]. Singhal et al. propose also a Context Awareness for Healthcare Service Delivery in asthma domain but they focus mainly on sensor capacity [20].
Many other researches have been conducted in the asthma field. Early life sensitization to indoor allergens is a predictor of asthma development later in life. Furthermore, the avoidance of exposure to these allergens continues to be important, especially given that the vast majority of children with asthma are sensitized to at least one indoor allergen [21]. Indoor allergens are of particular importance and principally include house dust mites, animal dander, cockroaches, mice, and molds [21][22]. The relative importance of these different allergens varies based on different environmental factors, depending on geographic, climatic, socioeconomic, and housing conditions. In the outdoors, pollens can induce seasonal asthma in sensitized individuals, and outdoor molds or fungi may lead to severe asthma exacerbations [23]. Changes in gaseous and particulate outdoor air pollutants are associated with daily asthmatic symptoms, a decrease in lung function, emergency room visits, and hospitalizations for asthma attacks [23]. Several studies have confirmed that air pollution from ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM) may induce or aggravate asthma [24]. The most important outdoor air pollutants are PM, O3, SO2, NO2, CO, and Lead (Pb). Exposure to environmental tobacco smoke (ETS) in early life, especially that from the mother, and maternal smoking during pregnancy, are known risk factors for respiratory symptoms and asthma among children. Active smoking has been shown to be risk factor for developing asthma; women who smoke are at particular risk. Similarly, second hand tobacco smoke (SHS) exposure is also associated with the development of asthma in adolescents and adults [8][25]. The workplace environment can lead to the development of different types of work-related asthma. Occupational asthma (OA) results from the exposure to irritants and allergens in the workplace, such as food derivatives, fumes, gases, chemical products, animal products, etc. [8][26]. Cold weather causes functional disabilities among individuals with an existing respiratory disease. This is because low temperatures and the accompanying low air humidity are likely to affect the respiratory epithelium and induce hyperresponsiveness and narrowing of the respiratory airways [27]. Cold temperatures can trigger asthma attacks. In general, the effect of cold weather appears to last for several weeks, whereas the effect of hot weather is more short term [23].
These research projects handle, in particular, the heterogeneity, yet, despite their medical value, they lack many aspects that may affect the lives of asthma patients. The developed frameworks and applications do not adequately support a comprehensive control of asthma either in terms of the indicators to be tracked or in terms of physical location changes. None of these research projects provide a detailed protection platform to monitor all relevant environmental triggers and food allergens. For example, a combination of the context categorization, formal context representation, and reasoning is not yet used. In addition, these frameworks need a personalized treatment asthma plan since each patient reacts differently to these triggers.

This entry is adapted from the peer-reviewed paper 10.3390/fi14070190

References

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