## 3 Related Works  
Machine learning methods have been applied to electronic health records (EHRs) for various clinical predictions [25; 26; 27]. These methods can handle high-dimensional data and find new features or nonlinear relationships in the data. However, issues related to data quality, such as missingness, misclassification, and measurement error, can impact the performance of these models [26]. Careful evaluation of the capabilities and limits of these models can help mitigate some of these concerns [26].  
While traditional machine learning algorithms and rule-based systems have been used for diagnostic tasks, they often require specific training on datasets with clear target classes and structured data [28; 29]. In contrast, LLMs offer the advantage of processing unstructured textual data directly, making them well-suited for handling the complexities of clinical reports. LLMs can capture nuanced linguistic patterns and contextual information, which are essential for accurate diagnostic predictions in cardiology [30; 31].  
Large language models (LLMs) have shown significant potential in various healthcare applications, including the analysis of clinical text for predicting patient outcomes [32; 33; 34; 35]. These models can analyze large amounts of data, identify patterns, and assist in the analysis and understanding of risk factors for diseases. The use of LLMs in diagnostic predictions has gained attention, particularly with the advent of models like GPT-4, Gemini, and Llama-3 [24; 36]. Domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs [37]. Research on the application of LLMs to non-English languages has been growing. For instance, clinical pre-trained language models can be used to analyze text from admission and medical reports to predict the probability of no coverage in a labor insurance process [38]. However, research on the performance of LLMs in diagnostic predictions in non-English languages, such as Spanish, remains limited.  
There are various parameters that impact the quality of the LLMs; among these, the use of prompt techniques and temperature have emerged as powerful approaches to leverage the capabilities of LLMs [39]. Prompt-based learning enables LLMs to perform prediction tasks by modifying the input using a template and filling in the unfilled information [39]. Temperature is a hyperparameter used in LLMs to control the randomness of the generated outputs [40]. Adjusting the temperature can impact the diversity and quality of the generated text. A recent study [40] investigated the effect of sampling temperature on the performance of LLMs on various problem-solving tasks. Although this study did not focus specifically on healthcare applications, its findings suggest that temperature may affect the performance of LLMs in diagnostic predictions.  
Despite the growing interest in applying LLMs to healthcare and non-English languages, there is a lack of research on the impact of prompt techniques and temperature on the performance of LLMs in diagnostic predictions in Spanish. This study aims to address this gap by evaluating the accuracy and reliability of LLMs in predicting medical diagnoses based on Spanish textual descriptions of symptoms while exploring the effects of prompt techniques and temperature on their performance. Our findings will contribute to the understanding of the strengths and limitations of LLMs in diagnostic predictions in Spanish and provide insights for future research in this area.