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Percorrer por autor "Leite, Francisca"

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  • Applications of large language models in cardiovascular disease: a systematic review
    Publication . Santos, José Ferreira; Ladeiras-Lopes, Ricardo; Leite, Francisca; Dores, Hélder
    Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Large language models (LLMs) offer potential solutions for enhancing patient education and supporting clinical decision-making. This study aimed to evaluate LLMs’ applications in CVD and explore their current implementation, from prevention to treatment. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this systematic review assessed LLM applications in CVD. A comprehensive PubMed search identified relevant studies. The review prioritized pragmatic and practical applications of LLMs. Key applications, benefits, and limitations of LLMs in CVD prevention were summarized. Thirty-five observational studies met the eligibility criteria. Of these, 54% addressed primary prevention and risk factor management, while 46% focused on established CVD. Commercial LLMs were evaluated in all but one study, with 91% (32 studies) assessing ChatGPT. The LLM applications were categorized as follows: 72% addressed patient education, 17% clinical decision support, and 11% both. In 68% of studies, the primary objective was to evaluate LLMs’ performance in answering frequently asked patient questions, with results indicating accurate, comprehensive, and generally safe responses. However, occasional misinformation and hallucinated references were noted. Additional applications included patient guidance on CVD, first aid, and lifestyle recommendations. Large language models were assessed for medical questions, diagnostic support, and treatment recommendations in clinical decision support. Large language models hold significant potential in CVD prevention and treatment. Evidence supports their potential as an alternative source of information for addressing patients’ questions about common CVD. However, further validation is needed for their application in individualized care, from diagnosis to treatment.
    2025-07-01Artigo de revisão Acesso aberto Ver mais
  • Zero-shot learning for clinical phenotyping: comparing LLMs and rule-based methods
    Publication . Neves, Bernardo; Moreira, José Maria; Gonçalves, Simão; Cerejo, Jorge; Silva, Nuno A. da; Leite, Francisca; Silva, Mário J.
    Background: Phenotyping, the process of systematically identifying and classifying conditions within clinical data, is a crucial first step in any data science work involving Electronic Health Records (EHRs). Traditional approaches require extensive manual annotation efforts and face challenges with scalability. Methods: We investigated the use of Large Language Models (LLMs) for zero-shot phenotyping of 20 prevalent chronic conditions based on synthetic patient summaries generated from real structured EHRs codes. We evaluated the performance of multiple LLMs, including GPT-4o, GPT-3.5, and LLaMA 3 models with 8-billion, 70-billion, and 405-billion parameters, comparing them against traditional rule-based methods. For the analysis we used a dataset of 1,000 patients from Hospital da Luz Lisboa. Results: GPT-4o outperformed both traditional rule-based methods and alternative LLMs, achieving superior recall (0.97) and macro-F1 score (0.92). Rule-based phenotyping, while highly precise (0.92), showed lower recall (0.36). The integration of rule-based methods with LLMs optimized phenotyping accuracy by targeting manual annotation efforts on discordant cases. Conclusion: Zero-shot learning with LLMs, particularly GPT-4o, offers a powerful and efficient approach for phenotyping chronic conditions from EHRs, significantly reducing the need for extensive labeled datasets while maintaining high accuracy and interpretability.
    2025-06-01Artigo de investigação Acesso aberto Ver mais