Background: The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain’s health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. Objective: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. Methods: After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. Results: After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. Conclusions: Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.

Background: Heart failure with preserved ejection fraction (HFpEF) is a major clinical manifestation of cardiac amyloidosis, a condition frequently underdiagnosed due to its nonspecific symptomatology. Electronic health records (EHRs) offer a promising avenue for supporting early symptom detection through natural language processing. However, identifying relevant clinical cues within unstructured narratives, particularly in Spanish, remains a significant challenge due to the scarcity of annotated corpora and domain-specific models. This study proposes and evaluates a Transformer-based natural language processing framework for automated detection of HFpEF-related symptoms in Spanish EHRs. Objective: The aim of this study is to assess the feasibility of leveraging unstructured clinical narratives to support early identification of heart failure phenotypes indicative of cardiac amyloidosis. It also examines how domain-specific language models and clinically guided optimization strategies can improve the reliability, sensitivity, and generalizability of symptom detection in real-world EHRs. Methods: A novel corpus of 15,304 Spanish clinical documents was manually annotated and validated by cardiology experts. The corpus was derived from the records of 262 patients (173 with suspected cardiac amyloidosis and 89 without). In total, 8 Transformer-based language models were evaluated, including general-purpose models, biomedical-specialized variants, and Longformers. Three clinically motivated optimization strategies were implemented to align models’ behavior with different diagnostic priorities: maximizing area under the curve (AUC) to enhance overall discrimination, optimizing F ₁ -score to balance sensitivity and precision, and prioritizing sensitivity to minimize false negatives. These strategies were independently applied during the fine-tuning of the models to assess their impact on performance under different clinical constraints. To ensure robust evaluation, testing was conducted on a dataset composed exclusively of previously unseen patients, allowing performance to be assessed under realistic and generalizable conditions. Results: All models achieved high performance, with AUC values above 0.940. The best-performing model, Longformer Biomedical-clinical , reached an AUC of 0.987, F ₁ -score of 0.985, sensitivity of 0.987, and specificity of 0.987 on the test dataset. Models optimized for sensitivity reduced the false-negative rate to under 3%, a key threshold for clinical safety. Comparative analyses confirmed that domain-adapted, long-sequence models are better suited for the semantic and structural complexity of Spanish clinical texts than general-purpose models. Conclusions: Transformer-based models can reliably detect HFpEF-related symptoms from Spanish EHRs, even in the presence of class imbalance and substantial linguistic complexity. The results show that combining domain-specific pretraining with long-context modeling architectures and clinically aligned optimization strategies leads to substantial gains in classification performance, particularly in sensitivity. These models not only achieve high accuracy and generalization on unseen patients but also demonstrate robustness in handling the semantic nuances and narrative structure of real-world clinical documentation. These findings support the potential deployment of Transformer-based systems as effective screening tools to prioritize patients at risk for cardiac amyloidosis in Spanish-speaking health care settings.

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models.