Dissertation/ Thesis
Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI ; Amélioration de Scores de Risque Environmental par Machine Learning Informé et AI Explicable
| العنوان: | Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI ; Amélioration de Scores de Risque Environmental par Machine Learning Informé et AI Explicable |
|---|---|
| المؤلفون: | Guimbaud, Jean-Baptiste |
| المساهمون: | Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard - Lyon I, Universitat Pompeu Fabra (Barcelone, Espagne), Rémy Cazabet, Léa Maitre |
| المصدر: | https://theses.hal.science/tel-04843974 ; Informatique [cs]. Université Claude Bernard - Lyon I; Universitat Pompeu Fabra (Barcelone, Espagne), 2024. Français. ⟨NNT : 2024LYO10188⟩. |
| بيانات النشر: | CCSD |
| سنة النشر: | 2024 |
| المجموعة: | HAL Lyon 1 (University Claude Bernard Lyon 1) |
| مصطلحات موضوعية: | Exposome, Explainable AI, Environmental risk scores, Informed Machine Learning, Deep Neural Networks, IA explicable, Scores de risques environnementaux, Machine Learning Informé, Réseaux de Neurones Profonds, [INFO]Computer Science [cs] |
| الوصف: | From conception onward, environmental factors such as air quality or dietary habits can significantly impact the risk of developing various chronic diseases. Within the epidemiological literature, indicators known as Environmental Risk Scores (ERSs) are used not only to identify individuals at risk but also to study the relationships between environmental factors and health. A limit of most ERSs is that they are expressed as linear combinations of a limited number of factors. This doctoral thesis aims to develop ERS indicators able to investigate nonlinear relationships and interactions across a broad range of exposures while discovering actionable factors to guide preventive measures and interventions, both in adults and children. To achieve this aim, we leverage the predictive abilities of non-parametric machine learning methods, combined with recent Explainable AI tools and existing domain knowledge. In the first part of this thesis, we compute machine learning-based environmental risk scores for mental, cardiometabolic, and respiratory general health for children. On top of identifying nonlinear relationships and exposure-exposure interactions, we identified new predictors of disease in childhood. The scores could explain a significant proportion of variance and their performances were stable across different cohorts. In the second part, we propose SEANN, a new approach integrating expert knowledge in the form of Pooled Effect Sizes (PESs) into the training of deep neural networks for the computation of extit{informed environmental risk scores}. SEANN aims to compute more robust ERSs, generalizable to a broader population, and able to capture exposure relationships that are closer to evidence known from the literature. We experimentally illustrate the approach's benefits using synthetic data, showing improved prediction generalizability in noisy contexts (i.e., observational settings) and improved reliability of interpretation using Explainable Artificial Intelligence (XAI) methods compared to an agnostic ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| اللغة: | French |
| Relation: | NNT: 2024LYO10188 |
| الاتاحة: | https://theses.hal.science/tel-04843974 https://theses.hal.science/tel-04843974v1/document https://theses.hal.science/tel-04843974v1/file/TH2024GUIMBAUDJEANBAPTISTE.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.9428B158 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |