# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 1 Introduction  
Technologies such as artificial intelligence and natural language processing have demonstrated significant promise in healthcare applications. Examples include utilizing machine learning techniques to identify latent diseases in X-ray images, predict patient outcomes, and provide treatment recommendations [1; 2; 3]. However, their widespread adoption is hindered by healthcare professionals' limited understanding of their potential and the scarcity of human resources knowledgeable in AI and machine learning. Additionally, a lack of interdisciplinary collaboration impedes the direct application of these technologies in healthcare [4]. One area where technology can be applied in healthcare is supporting strategies to manage chronic diseases. In recent times, chronic diseases have experienced an increase globally due to longer life expectancy, sedentary lifestyles, and air pollution. This rise has led to a greater demand for health services [5; 6]. Asthma is a chronic condition considered a severe public health problem, affecting around 200 million people globally [7]. People who have asthma and do not adhere to their treatment experience a progressive decrease in their quality of life. Patients who adhere to inhaler treatment and a proper application technique exhibit better symptom control [8]. Adherence to treatment in people with chronic diseases is around 50%; however, in people with asthma, this percentage is even lower, as low as 22% [9]. Therefore, healthcare professionals must promote medical indications comprehension among these patients, which is not trivial. Recent studies indicate that 78% of patients do not recall medical instructions, 40% to 80% of communicated information is immediately forgotten, and 50% of retained information might be incorrect [10]. Consequently, developing tools that can support healthcare professionals in patient education and patients in understanding and remembering medical instructions is essential. In this context, the use of pictograms associated with medical instructions has shown promise in aiding individuals with low health literacy in adhering to medical treatment [11]. However, before evaluating this positive effect, a proper software framework must be developed to accurately translate instructions from natural language into pictograms or pictogram sequences. This task is non-trivial due to variations in how health professionals convey their instructions--some use medication abbreviations, while others use special lexical structures to indicate dosage, posing challenges for developing accurate mapping rules. The literature on the automated translation of text to images is currently dominated by methods based on neural networks, specifically Generative Adversarial Networks (GANs) [12; 13; 14]. However, these neural network-based methods require vast amounts of data, which are not always readily available, to create an accurate model. In the case of pictograms, the literature is not as extensive. For instance, Pico, a tool that transforms text to pictograms and vice versa, utilizes two sets of pictograms, Beta and Sclera, in conjunction with WordNet to construct a lexical-semantic database for automatically associating pictograms with text [15]. Another work in this line is Pictogrammar, an adaptive and augmentative pictogram-based communication system designed for individuals with autism spectrum disorder to assist them in generating messages [16]. Pictogrammar employs Pictontology, a custom-built ontology of multimedia resources based on 621 SupO (Simple Upper Ontology) terms linked to pictograms, along with the attributes and properties of each resource. This model produces restrictions that facilitate the addition of new concepts into categories, as well as the suggestion of new pictograms based on the context (predictive semantic grammar). In this context, Glyph stands out as one of the pioneering tools used to transform text into pictograms in the healthcare field, serving as the basis for various articles [17; 18].