# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 4 Discussion  
In this research, we introduce SIMAP, a technological framework designed to promote adherence among asthma patients through the utilization of pictograms. This framework was built using a user-centered methodology. Its components are a collection of clinical recommendations for treating asthma, a set of pictograms to accompany these clinical recommendations, and an algorithm designed to use natural language processing to automatically enhance the clinical instructions for asthma treatment. \begin{table}
\begin{tabular}{c c} \hline \hline
**\% Error per Character** & **Correct Translation Rate** \\ \hline
1\% & 96.4\% \\
5\% & 93.8\% \\
10\% & 84.5\% \\
20\% & 62.9\% \\
30\% & 40.2\% \\
40\% & 22.5\% \\
50\% & 11.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Correct translation rate for different error probabilities per each character. \begin{table}
\begin{tabular}{c c} \hline \hline
**\% Error per Character** & **Correct Translation Rate** \\ \hline
1\% & 96.5\% \\
5\% & 95.3\% \\
10\% & 91\% \\
20\% & 78.3\% \\
30\% & 61.9\% \\
40\% & 41.9\% \\
50\% & 28.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Correct translation rate for different error probabilities per text character. The methodology to validate both the medical indications and pictograms is intended to aid in the treatment of patients with bronchial asthma. Our observations revealed that most of the pictograms were readily comprehended by the patients upon initial presentation. However, those pictograms that posed challenges in terms of understanding were subjected to redesign and subsequent validation. Through this iterative process, we successfully validated a total of 18 pictograms, which subsequently served as the foundation for the algorithm's design. On the other hand, the algorithm to automatically associate medical indications with pictograms maps medical indications to sequences of previously validated pictograms. In crafting this algorithm, we harness natural language processing tools to meticulously analyze and standardize textual data, streamlining its correlation with one of the 18 available pictograms. These pictograms indicate the dosage, type of medicine, and its frequency in a dataset of medical indications, achieving a 96% correct translation rate. One of the main challenges in achieving a high accuracy rate (96%) in the assignment of medical indications to pictograms was the variability in the way healthcare professionals wrote the indications. This variability included the use of abbreviations and specific terminology that the model did not easily recognize, creating difficulties in correctly interpreting and mapping the instructions to the corresponding pictograms. To overcome this challenge, an exhaustive analysis of common medical indications in asthma treatment was conducted, with the active participation of healthcare professionals to validate and document the various forms of expression. Additionally, a natural language processing (NLP) algorithm was developed to analyze, tokenize, and map words and their context to pictograms, handling both structured and unstructured text. Specific pictogram dictionaries were created that included multiple variants of writing for each medication or instruction, improving mapping accuracy through tokenization and n-gram techniques. Iterative validation and refinement processes, in collaboration with healthcare professionals and patients, allowed continuous adjustment and improvement of the system, ensuring that the indications were clearly represented through the pictograms. The combination of these approaches was key to overcoming the challenges associated with variability in the writing of indications and achieving a high accuracy rate. Although SIMAP was designed to complement medical indications for patients with bronchial asthma, the pictograms related to the inhaler technique could be adapted to other respiratory pathologies and the frequency pictograms, such as every 8 h, could be adapted to other pathologies.