AIMC Topic: Paris
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Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data.
International journal of environmental research and public health
Feb 27, 2025
Epidemiologists often handle large datasets with numerous variables and are currently seeing a growing wealth of techniques for data analysis, such as machine learning. Critical aspects involve addressing causality, often based on observational data,...
G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape.
Journal of environmental management
Aug 6, 2024
The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategie...
Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis.
PloS one
Feb 22, 2022
Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintain...
Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.
PloS one
Jan 19, 2018
PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant am...
A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2020
OBJECTIVE: To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.
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