## 3 Results and Discussion
This research is part of a larger initiative aimed at developing computational intelligence tools to assist healthcare professionals in diagnosing and managing suspected PTB cases more effectively. The tuberculosis diagnosis program at Hospital Santa Clara relies on two distinct sources of data. The first is structured data collected by nursing professionals, who record acquisition variables based on test results and patient interviews. The second source consists of unstructured data documented by physicians in text reports during patient appointments. These two sources are largely independent and collected at different moments. While they are eventually integrated into the health record through the information system, timely TB diagnosis is crucial for isolating suspected patients and initiating anti-TB therapy. This study aims to determine whether both data sources are complementary or if one alone is sufficient for diagnosis. Additionally, the proposal seeks to provide an alternative for remote areas where limited digital health infrastructure makes maintaining a comprehensive electronic health record challenging. To enhance the paper, these details have been highlighted in red in the discussion section, following the previous comment. A key consideration in processing data for NLP applications is the comparison between TF-IDF and embedding techniques. In this study, TF-IDF demonstrated superior performance over embeddings (see Tables 3 and 4 ), particularly in the CD-based approach,where isolated models performed better with TF-IDF. This outcome may be attributed to the lower complexity of the classification method, a trend also observed in the fusion strategies. Specifically, Type A, which employs a simpler approach, outperformed the more complex Type B strategy. There, three out of five machine learning models achieved better performance than more structured and computationally intensive models. Regarding performance (see Figures 6 and 7), this phenomenon is evident, as the Type A fusion achieved better ROC curves compared to other approaches. The use of NLP and LLMs in TB diagnosis has been explored in various contexts. These include cough signal analysis and embeddings for detection [49], as well as a feature fusion approach that combines cough signals with spectrogram images [50], achieving a sensitivity of 98.13% from 144 signals. Additionally, text analysis has been used to differentiate TB from COVID-19 based on reported cases [51]. Other studies have compared NLP-based image analysis with radiologist-generated natural text from chest radiographs (CXRs) [34] and developed a report generation system for lung diseases by fusing text reports with CXR images, achieving an accuracy of 94% on a dataset of 3955 images [52]. Furthermore, laboratory test results, tumor markers, and imaging analysis were used to classify 296 patients with spinal TB and spinal tumors using ChatGPT (Version 4) and ML models. The results showed that ChatGPT's performance was suboptimal, with a sensitivity of 71.67% [53]. While similar studies have explored the fusion of structured and unstructured data for TB prediction--achieving a mean AUC of 95.5% across different models [54]--the present study's approach, which focuses on basic text from a specific segment of the EMR, remains largely unexplored. Notably, the aforementioned study analyzed 692,949 patients and incorporated more advanced clinical variables (e.g., hemoptsis, cough, and erythrocyte sedimentation rate), making direct comparisons challenging. Additionally, a multimodal approach leveraging EHR datasets, various clinical notes, LLMs, and hypergraph modeling of structured EHR data was introduced by [31]. Although this method achieved AUC values of 83.54% and 73.01% on two different datasets, it was applied outside the TB domain. A pre-trained model combining text reports and CXR images achieved a validation accuracy of 94% [52], though a notable distinction was the use of approximately 3800 images from a well-established dataset.