Browsing by Author "Rodrigues, Pedro Pereira"
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- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined bayesian network toolPublication . Caixinha Algarvio, Rogério; Conceição, Jaime; Rodrigues, Pedro Pereira; Ribeiro, Inês; Silva, Renato Ferreira daBackground Pharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring drug safety. Traditional methods are slow and inconsistent, but artificial intelligence (AI), through automation and advanced analytics, improves efficiency and accuracy in managing increasing data complexity. Aim To explore AI’s practical applications in pharmacovigilance, focusing on efficiency, process acceleration, and task automation. It also examines the use of an expert-defined Bayesian network for causality assessment in a Pharmacovigilance Centre, demonstrating its impact on decision-making. Method A comprehensive literature narrative review was conducted in MEDLINE (via PubMed), Scopus, and Web of Science using a set of targeted keywords, including but not limited to “pharmacovigilance”, “artificial intelligence”, “adverse drug reactions” and “drug safety”. Relevant studies were analysed without restrictions on publication year or language. The search was carried out in January 2025. Results AI has greatly improved pharmacovigilance by streamlining signal detection, surveillance, and ADR reporting automation. Techniques like data mining and automated signal detection have expedited safety signal identification, while duplicate detection has enhanced data precision in safety evaluations. AI has also refined real-world evidence analysis, deepening drug safety and efficacy insights. Predictive models now anticipate ADRs and drug-drug interactions, enabling proactive patient care. At a regional pharmacovigilance center, the implementation of an expert-defined Bayesian network has optimized causality assessment, reducing processing times from days to hours, minimizing subjectivity, and improving the reliability of drug safety evaluations. Conclusion AI holds significant promise for enhancing pharmacovigilance practices, yet its practical application remains primarily confined to academic research, with integration hindered by data quality issues, regulatory barriers, and the need for more transparent algorithms.
- Development and validation of risk matrices for Crohn's Disease outcomes in patients who underwent early therapeutic interventionsPublication . Dias, Cláudia Camila; Rodrigues, Pedro Pereira; Coelho, Rosa; Santos, Paula Moura; Fernandes, Samuel; Lago, Paula; Caetano, Cidalina; Rodrigues, Angela; Portela, Francisco; Oliveira, Ana; Ministro, Paula; Cancela, Eugenia; Vieira, Ana Isabel; Barosa, Rita; Cotter, Jose; Carvalho, Pedro; Cremers, Isabelle; Trabulo, Daniel; Caldeira, Paulo; Antunes, Artur; Rosa, Isadora; Moleiro, Joana; Peixe, Paula; Herculano, Rita; Gonçalves, Raquel; Gonçalves, Bruno; Sousa, Helena Tavares; Contente, Luis; Morna, Henrique; Lopes, Susana; Magro, FernandoIntroduction: The establishment of prognostic models for Crohn's disease [CD] is highly desirable, as they have the potential to guide physicians in the decision-making process concerning therapeutic choices, thus improving patients' health and quality of life. Our aim was to derive models for disabling CD and reoperation based solely on clinical/demographic data. Methods: A multicentric and retrospectively enrolled cohort of CD patients, subject to early surgery or immunosuppression, was analysed in order to build Bayesian network models and risk matrices. The final results were validated internally and with a multicentric and prospectively enrolled cohort. Results: The derivation cohort included a total of 489 CD patients [64% with disabling disease and 18% who needed reoperation], while the validation cohort included 129 CD patients with similar outcome proportions. The Bayesian models achieved an area under the curve of 78% for disabling disease and 86% for reoperation. Age at diagnosis, perianal disease, disease aggressiveness and early therapeutic decisions were found to be significant factors, and were used to construct user-friendly matrices depicting the probability of each outcome in patients with various combinations of these factors. The matrices exhibit good performance for the most important criteria: disabling disease positive post-test odds = 8.00 [2.72-23.44] and reoperation negative post-test odds = 0.02 [0.00-0.11]. Conclusions: Clinical and demographical risk factors for disabling CD and reoperation were determined and their impact was quantified by means of risk matrices, which are applicable as bedside clinical tools that can help physicians during therapeutic decisions in early disease management.