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DTSTART;TZID=Europe/Paris:20250929T140000
DTEND;TZID=Europe/Paris:20251006T153000
DTSTAMP:20260614T040743
CREATED:20250929T074358Z
LAST-MODIFIED:20250929T074457Z
UID:1797-1759154400-1759764600@www.limics.fr
SUMMARY:Thomas Meyer : Symbolic AI in the era of LLMs
DESCRIPTION:Titre: Symbolic AI in the era of LLMs\n\nAbstract:\nRecent spectacular advances in Artificial Intelligence (AI) focus strongly on the sub-area of AI known as machine learning. In this talk\, I will remind participants that there are several other sub-disciplines of AI\, and that it is important for the field to maintain and grow expertise in all aspects of AI. I will then proceed to practise what I preach by introducing Symbolic AI as one of the pillars of AI. The talk will conclude with some suggestions on how Symbolic AI can benefit from research in machine learning\, and vice versa.\n\nBio-sketch:\nProfessor Thomas (Tommie) Meyer is a professor in Computer Science at UCT\, the NRF SARChI research chair in Symbolic Artificial Intelligence\, the co-director of the national Centre for Artificial Intelligence Research\, and one of only three South African Computer Scientists to have been awarded an A rating by the NRF. He is the (co-) author of more than 200 peer-reviewed research outputs and has overseen 7 postdoctoral fellowships\, and supervised 7 PhD students and 20 Master’s students to completion. He is currently supervising 6 PhD students and 9 Master’s students. He is an elected member of the Academy of Science of South Africa\, an elected fellow of the African Academy of Sciences\, and the recipient of the 2025 Pioneer Award of the South African Institute for Computer Scientists and Information Technologists.
URL:https://www.limics.fr/event/thomas-meyer-symbolic-ai-in-the-era-of-llms/
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DTSTART;TZID=Europe/Paris:20251013T140000
DTEND;TZID=Europe/Paris:20251013T153000
DTSTAMP:20260614T040743
CREATED:20250611T095604Z
LAST-MODIFIED:20250703T075216Z
UID:1723-1760364000-1760369400@www.limics.fr
SUMMARY:Jérémy Florence\, Univ. Paris Cité: Refining risk stratification using CMR in patients with hypertrophic cardiomyopathy
DESCRIPTION:Title: « Refining risk stratification using CMR in patients with hypertrophic cardiomyopathy ».\nAbstract: Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease\, characterized by ventricular hypertrophy\, myocyte disarray\, and interstitial fibrosis\, often resulting from sarcomere gene variants. Patients experience adverse events such as heart failure (HF)\, stroke\, arrythmias and sudden cardiac death (SCD) leading to an increased risk of mortality. Late gadolinium enhancement (LGE) detected by cardiovascular magnetic resonance (CMR) imaging has emerged as a valuable method for the quantification of myocardial scarring. The presence and extent of LGE are both associated with a heightened risk of all-cause mortality in HCM patients. However\, despite broad acceptance and recent inclusion in the ACC/AHA and ESC guidelines\, optimal cutoff values for LGE quantification remain debated. Furthermore\, several studies on non-ischemic cardiomyopathies have demonstrated that specific features of LGE\, such as its location and pattern\, may play a crucial role in risk stratification\, which is pivotal for providing tailored care and optimized decision-making regarding medications and preventive implantable cardioverter-defibrillator (ICD). However\, there is a lack of data on the prognostic impact of these specific LGE features in HCM and very few studies have explored simultaneously the role of LGE extent\, location and pattern in predicting outcomes for this population. Given the importance of improving risk stratification in HCM patients to guide personalized treatment decisions\, particularly regarding medications and ICD\, our aim is to assess the incremental prognostic value of the “LGE granularity” concept —encompassing LGE extent\, location\, and pattern — in predicting all-cause mortality in a patients with HCM.
URL:https://www.limics.fr/event/jeremy-florence/
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DTSTART;TZID=Europe/Paris:20251031T140000
DTEND;TZID=Europe/Paris:20251031T190000
DTSTAMP:20260614T040743
CREATED:20251031T103858Z
LAST-MODIFIED:20251120T084546Z
UID:1802-1761919200-1761937200@www.limics.fr
SUMMARY:Soutenance de thèse Stella Dimitsaki
DESCRIPTION:Title: Use of Artificial Intelligence for the Analysis of Potential Pharmacovigilance Signals upon Real-World data \nLieu: Limics\, Campus des Cordeliers\, salle de conférence \n\n\n\nRésumé :\n\n\nCette thèse explore comment l’intelligence artificielle (IA)\, en particulier l’apprentissage automatique causal (CML)\, peut améliorer la pharmacovigilance en exploitant des données structurées issues du monde réel (RWD)\, telles que les dossiers médicaux électroniques. Alors que les méthodes traditionnelles reposant sur la déclaration spontanée d’effets indésirables médicamenteux sont limitées par la sous-déclaration et les biais\, les RWD offrent une vision plus complète de l’expérience des patients\, mais se heurtent à des problèmes de complexité et de qualité des données. Une étude exploratoire a révélé les principaux obstacles aux applications actuelles de l’IA\, notamment le manque de cohérence dans le prétraitement des données et le manque d’explicabilité. Pour y remédier\, notre travail de thèse a mis en œuvre un cadre CML utilisant la base de données MIMIC-IV afin de détecter les lésions rénales aiguës d’origine médicamenteuse\, démontrant ainsi que le CML peut fournir des informations interprétables\, personnalisées et cliniquement pertinentes. Les résultats positionnent l’IA causale comme une voie prometteuse pour améliorer la précision\, la transparence et l’acceptation réglementaire des systèmes de pharmacovigilance.\n\n\n\n\n\nSummary:\n\n\nThis thesis examines how artificial intelligence (AI)\, specifically causal machine learning (CML)\, can enhance pharmacovigilance by utilizing structured real-world data (RWD)\, including electronic health records. While traditional methods\, which rely on spontaneous reporting of adverse drug reactions\, are limited by underreporting and bias\, RWD offers a more comprehensive view of the patient experience\, but faces challenges related to data complexity and quality. An exploratory study revealed the main obstacles to current AI applications\, including a lack of consistency in data preprocessing and a lack of explainability. To address this\, our thesis work implemented a CML framework using the MIMIC-IV database to detect acute drug-induced kidney injury\, demonstrating that CML can provide interpretable\, personalized\, and clinically relevant information. The results position causal AI as a promising avenue for improving the accuracy\, transparency\, and regulatory acceptance of pharmacovigilance systems.
URL:https://www.limics.fr/event/soutenance-de-these-stella-dimitsaki/
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