## 1 Introduction
Previous research conducted by the same team has explored alternative approaches for addressing these challenges in [11; 12; 13]. In this study, an extension was based on a previously reported method, where medical staff documented patient consultations with detailed descriptions of medical findings and terminology. This documentation, commonly used in the traditional Colombian healthcare system, can serve as an additional data source to support the diagnostic process. In recent years, artificial intelligence (AI) has been increasingly utilized in medicine to support decision-making [14; 15]. AI-powered systems provide healthcare professionals with valuable insights, enabling them to enhance diagnostic accuracy and efficiency. Thesesystems can process large amounts of data and apply machine learning (ML) techniques to specific medical tasks [16; 17]. One of the key advantages of AI-based tools is the cost-effectiveness and accessibility for frontline healthcare workers, making them particularly useful in situations where conventional diagnostic methods are unavailable [18]. AI has been successfully applied in various medical fields, including cancer detection [19], COVID-19 diagnosis and treatment, and drug discovery through deep neural networks [20; 21; 22; 23]. In the case of TB, studies [12; 24] have demonstrated how artificial neural networks (ANNs) can be trained to diagnose TB using clinical data (CD). Additionally, research in [22; 25] explores ANN-based clustering techniques to categorize populations into three risk groups (high, medium, and low risk) showing promising results in TB and COVID-19 risk assessment. Natural language processing (NLP) is a branch of AI that enables the analysis of unstructured text and can be used to develop decision-support tools. For example, NLP has been applied to build AI systems that assist with tasks such as retrieving relevant information [26], determining patient eligibility and tracking [27; 28], and diagnosing diseases [29]. These systems typically rely on data from electronic medical records (EMRs), including clinical notes (CNs), laboratory results, and imaging data. Research has shown that the most effective NLP models are those that learn patterns from data using ML techniques [30]. Since 2021, the rise of large language models (LLMs) has driven advancements in generative models and new approaches to text processing. However, these methods face challenges related to the specific dataset used for model development and the manual annotations provided by expert medical professionals [31; 32]. Examples of NLP applications in healthcare include assessing dietary risks for diabetes patients [33] and comparing NLP-generated labels based on medical imaging with expert TB screening in chest X-rays [34]. Additionally, NLP and LLM have been used for data fusion in medical contexts [31; 32; 35]. To enhance PTB diagnosis, we proposed four predictive modeling schemes utilizing two data sources. The first source comprises clinical data (CD) from 151 patients, including variables such as HIV status, geographic location within the city, and sex. Additionally, clinical notes (CNs) from physicians' reports (PRs) during patient consultations as part of routine practice were included. Two schemes integrate both CD and CN, while the other two process them separately. Each scheme employs five machine learning (ML) algorithms: support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), and artificial neural networks (ANNs). Since CN consists of unstructured text, NLP techniques were applied to convert these reports into numerical representations, allowing ML algorithms to extract meaningful patterns. This approach addresses the challenges faced by resource-limited settings, where access to advanced diagnostic infrastructure is scarce. By leveraging data fusion techniques and computational intelligence, the proposed models can support TB diagnosis in regions lacking well-developed healthcare systems, improving early detection and intervention.