Publications by authors named "D Benavent"
Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support.
View Article and Find Full Text PDFCorrection: Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.
Clin Transl Oncol
November 2024
Article Synopsis
- A predictive model was created to estimate the risk of major bleeding in cancer patients undergoing anticoagulant treatment for venous thromboembolism (VTE) within six months following their diagnosis.
- The study analyzed data from electronic health records across nine hospitals in Spain, using natural language processing and machine learning to identify key predictors of bleeding and develop various predictive algorithms.
- Findings indicated that about 10.9% of the patients experienced major bleeding events after VTE diagnosis, with significant predictors being factors like hemoglobin levels and age, and the new models outperformed the existing CAT-BLEED score.
Introduction: Data on prevalence of fatigue in rheumatoid arthritis (RA) patients in the era of biological treatments remains scarce, with a lack of case-control studies. This study evaluates the prevalence of fatigue in Spanish women over 50 years with RA using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, explores its association with RA-related variables, and seeks to identify the primary factors influencing fatigue. Ultimately, our objective is to underscore the clinical significance of fatigue as a comorbidity and to advocate for its systematic evaluation in routine clinical practice.
View Article and Find Full Text PDFBackground: Since the publication of the 2011 European Alliance of Associations for Rheumatology (EULAR) recommendations for patient research partner (PRP) involvement in rheumatology research, the role of PRPs has evolved considerably. Therefore, an update of the 2011 recommendations was deemed necessary.
Methods: In accordance with the EULAR Standardised Operational Procedures, a task force comprising 13 researchers, 2 health professionals and 10 PRPs was convened.