## 4 Conclusions  
The aim of this study is to explore computational intelligence tools as alternatives for providing health professionals with additional information on PTB diagnosis. The proposed approach is particularly useful in decision-making scenarios where resources are limited, and standard diagnostic tests are not readily available. This work leverages two sources of information: clinical data and physicians' reports extracted from restricted patient medical data. Various ML models were applied to each data source, along with two data fusion approaches based on text representation. Models that relied solely on physicians' reports performed 9% worse than those using clinical data but achieved higher sensitivity (73%). While data fusion models did not surpass the performance of models based solely on clinical variables, they still demonstrated notable sensitivity. However, as discussed, clinical variables are not always accessible, and specific constraints must be considered. In such cases, NLP techniques play a crucial role by offering an alternative when patient data are incomplete or when individuals are unwilling to disclose personal information. Both fusion approaches achieved AUC values of 69.3% and 67.2%, outperforming smear microscopy, which remains the primary diagnostic option in resource-limited regions. Additionally, the embedding technique for text vectorization yielded lower performance compared to TF-IDF, which achieved an AUC of 60.8%. This suggests that TF-IDF may be a more effective method for representing textual data in the context of TB diagnosis. The insights gained and the algorithms developed in this study contribute to future research efforts aimed at refining these approaches. Ultimately, this work lays the foundation for integrating NLP-driven tools into the workflow of healthcare professionals to enhance PTB diagnosis. As a future direction, there is an opportunity to analyze additional data sources that could enhance diagnostic accuracy. Incorporating laboratory test results, clinical examination findings, and other accessible diagnostic methods could provide valuable insights, particularly in remote regions with limited healthcare infrastructure. Additionally, when available, CXR images could serve as a complementary tool for improving TB detection and supporting clinical decision-making. **Author Contributions:** Conceptualization, A.D.O.-C. and A.F.R.-G.; methodology, A.D.O.-C. and A.F.R.-G.; validation, A.D.O.-C., A.F.R.-G., A.L.J., C.E.A., M.A.P. and E.V. ; formal analysis, A.D.O.-C., A.F.R.-G., A.L.J., C.E.A., M.A.P. and E.V. ; investigation, A.D.O.-C., A.F.R.-G., A.L.J., C.E.A., M.A.P. and E.V. ; resources, C.E.A., M.A.P. and E.V. ; data curation, A.D.O.-C., A.F.R.-G. and A.L.J. ; writing--original draft preparation, A.D.O.-C. and A.F.R.-G.; writing--review and editing, A.D.O.-C. and A.L.J. ; visualization, A.D.O.-C. and A.F.R.-G.; supervision, A.D.O.-C., A.L.J., C.E.A., M.A.P. and E.V. ; project administration, A.L.J. and A.D.O.-C.; funding acquisition, A.D.O.-C., E.V. and C.E.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by MinCiencias (_Ministerio de Ciencia, Tecnologia e Innovacion de Colombia_) grant number 123380762899 CT 819-2018, Subred Integrada de Servicios de Salud Centro-Orientte E.S.E, and Universidad Antonio Narino, and the APC was funded by Universidad del Rosario. **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Subred Integrada de Servicios de Salud Centro-Orientte E.S.E with protocol code CEI. 0106/2021, act number 316, and date of approval 27 May 2021. **Informed Consent Statement:** Informed consent was not required, as all data were retrospective and fully anonymized. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.