# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 5 Conclusions  
Our successful implementation of a development and validation strategy has yielded a robust framework, laying the foundation for substantial progress. All medical instructions and pictograms comprising SIMAP were validated for both their correspondence and comprehension by the patients. Likewise, the SIMAP algorithm has demonstrated an impressive accuracy rate of approximately 96% when tested on medical indications extracted from a clinical dataset. Consequently, we firmly believe that this framework holdsimmense potential for significantly improving the treatment of asthma patients, thereby promising enhanced healthcare outcomes. In our future endeavors, we aim to expand our pictogram database, with a specific emphasis on enriching resources for asthma patients. We also will conduct feasibility assessments to explore the potential inclusion of other medical conditions, such as diabetes, within the scope of our project. SIMAP was developed as a proof of concept with medical indications and pictograms related to the treatment of bronchial asthma, so the product achieved is a pilot system only evaluated at the laboratory level in terms of its accuracy. The following steps evaluate the system in a relevant environment to prove its effectiveness in controlling bronchial asthma. On the other hand, plans also include generating a classification and scalability strategy that allows, in collaboration with health centers, other universities, and even other countries, to add medical indications and pictograms for other chronic pathologies or health conditions. As for the future development of the SIMAP algorithm, it is planned to explore the implementation of advanced techniques such as word embeddings and more sophisticated language models to improve the accuracy and robustness of the system. Using embeddings such as Word2 Vec, GloVe, or BERT can provide a better semantic representation of words and phrases in medical indications, which could significantly improve the algorithm's ability to handle variations in natural language. In addition, incorporating transformer-based models, such as GPT-3 or BERT, can increase the understanding and processing of unstructured medical texts. These advanced techniques will allow the algorithm to learn from large volumes of textual data and continuously improve its performance using deep learning. It is also planned to use transfer learning techniques to adapt the algorithm to different medical specialties, ensuring its applicability to a broader range of medical conditions. These enhancements will not only increase the accuracy of the mapping of indications to pictograms but will also facilitate the scalability of the system and its integration into various clinical contexts. Finally, we plan to incorporate SIMAP into mobile applications for patients, aiming to facilitate treatment adherence by providing information through various means. Conceptualization, R.F., C.T., M.E.L., J.G., and C.R. ; Methodology, R.F., C.T., M.E.L., F.M., C.R., J.N., J.G., E.P., G.N., J.P., C.P., and D.H.; Writing--original draft preparation, R.F., M.E.L., and J.P.; Writing--review and editing, C.T., F.M., C.R., J.G., J.N., E.P., G.N., and J.P.; Supervision, R.F. and C.T. ; Funding acquisition, R.F. and C.T. All authors have read and agreed to the published version of the manuscript. This research was funded by Universidad de Concepcion grant number 219.092.053-M, FONDEF, ANID (National Research and Development Agency, Chile, Funder URL [[https://anid.cl/](https://anid.cl/)]([https://anid.cl/](https://anid.cl/)), accessed on 17 July 2024), grant number ID19110120 (RLFI, CT, ML, CR, JG, EP, JN, JP, FM, GN), the National Center on Health Information Systems (CTI230006 CENS), and the Millennium Nucleus on Sociomedicine, grant ANID-MILENIO-NCS2021_013 (CT, RLFI, ML). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.