# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 3 Results
### SIMAP Algorithm Results
The eight instances where SIMAP did not generate pictograms corresponded to instructions that did not prescribe any treatment (e.g., \"sin-tratamiento\"). In our second evaluation, we aimed to assess the robustness of SIMAP's mapping algorithm. Specifically, we sought to determine how well SIMAP performed when confronted with typographical errors introduced into medical indications by health professionals or their assistants. These errors could potentially result in the failure to generate pictograms. For this assessment, we intentionally introduced random errors into the medical indications and measured the accuracy of translation in the resulting erroneous indications. We assigned a probability to each character in the text that referred to the prescribed medicine for the patient's treatment, indicating the likelihood of it being replaced by another random letter. For instance, \"BUD 2-0-2\" could be altered to \"ByD 2-0-2\". To ensure the credibility of our findings and to mitigate the influence of randomness, we conducted this experiment multiple times for each probability value. Subsequently, we report the average results in Table 21, providing a more robust assessment of SIMAP's performance under these challenging conditions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & **Output** \\ \hline \hline BUD 202 & Budesonide \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Input and Output obtained from the system. Table 21 provides valuable insights into SIMAP's performance, illustrating its capability to accurately map erroneous medical indications to the correct pictogram. SIMAP achieves an accuracy rate of nearly 80% when, on average, one out of every five characters contains an error in its writing. However, as the error rate escalates, the correct translation rate experiences a notable decline. Even when approximately half of the characters contain errors, SIMAP still manages to correctly map nearly 30% of the indications. In our third and final evaluation, we expanded upon the second evaluation by broadening the scope of potential typos to encompass all characters within the medical indications, not just those indicating the medicine to be administered. This extended evaluation allowed for the possibility of spaces, lines, and numbers being replaced by random letters with specific probabilities. To maintain robustness and mitigate the influence of random errors, we conducted each experiment 10 times, providing a more comprehensive view of SIMAP's performance, as detailed in Table 22.  
The outcomes presented in Table 22 align with our expectations, clearly indicating that SIMAP's proficiency in accurately mapping indications to pictograms diminishes as the number of errors in the indications increases. Nevertheless, it is noteworthy that SIMAP can still achieve an 85% accuracy rate in correct translations when there is an average 10% chance of each character being erroneous. These findings underscore SIMAP's capacity to effectively enhance medical indications with pictograms that faithfully represent their intended meaning. However, we must emphasize the importance of maintaining a standardized input format for instructions when employing SIMAP for mapping to pictograms. As evidenced by the results, SIMAP exhibits robustness against typos up to a certain threshold. Beyond that threshold, when indications contain a substantial number of typos, the algorithm faces significant challenges in producing the correct corresponding pictogram(s).