A5019573436- Air Quality Monitoring and Forecasting
- Radiomics and Machine Learning in Medical Imaging
- Colorectal Cancer Screening and Detection
- Computational Physics and Python Applications
- Cancer, Lipids, and Metabolism
- Telemedicine and Telehealth Implementation
- Genetic factors in colorectal cancer
- Air Quality and Health Impacts
- Agriculture and Rural Development Research
- Cutaneous Melanoma Detection and Management
- Traffic Prediction and Management Techniques
- Statistical Methods and Bayesian Inference
- Machine Learning in Healthcare
- Statistical Methods in Clinical Trials
- Machine Learning and Data Classification
- Global Cancer Incidence and Screening
- Cutaneous lymphoproliferative disorders research
- Mycobacterium research and diagnosis
- Healthcare Systems and Practices
- COVID-19 and healthcare impacts
- Atmospheric chemistry and aerosols
- Climate Change and Health Impacts
- Energy and Environment Impacts
- School Health and Nursing Education
- Generative Adversarial Networks and Image Synthesis
Economic & Social Sciences, Health Systems & Medical Informatics
2023-2025
Inserm
2023-2025
Université Mohammed VI des Sciences de la Santé
2022-2025
Bridge University
2023
Centre Hospitalier Universitaire Hassan II
2023
Institut de Recherche pour le Développement
2023
IMT Atlantique
2022
Laboratoire d’Économie et de Gestion de l'Ouest
2021
Abstract The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years and identify associated strong prognostic factors among patients in Morocco through an interpretable machine learning approach. This approach is based on a fully non-parametric random forest (RSF), incorporating variable importance partial dependence effects. data povided from retrospective 343 diagnosed followed Hassan II University Hospital. Covariate selection performed using...
<title>Abstract</title> The aim of our study was to assess the overall survival rates for colorectal patients in Morocco and identify strong prognostic factors using a novel approach combining random forest Cox model. Covariate selection performed variable importance based on permutation partial dependence plots were displayed explore depth relationship between estimated effect given predictor rates. predictive performance measured by two metrics, Concordance Index (C-index) Brier Score...
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes performance metrics used evaluate ML methods: post-imputation bias regression estimates, predictive accuracy, substantive model-free metrics. As illustration, we applied...
The private healthcare sector has become an essential component of systems globally. This interest increased with the universal health coverage agenda. However, in most low- and middle-income countries, few classificatory studies hospital were carried out. study describes a developing country setup propose typology that could facilitate identification its categories understanding organizational strategic characteristics. All hospitals Morocco as December 31, 2021 including 397 facilities are...
Background: Recent technological advances have paved the way for a new modality of medical practice known as teleconsultation. Positive perceptions about benefits teleconsultation and its acceptance by clinicians are key predictors uptake. The aim this study was to assess knowledge, perceptions, acceptability among Moroccan physicians. Methods: This is descriptive cross-sectional conducted at Cheikh Khalifa International University Hospital (HCK) Casablanca, Morocco. Study participants...
BACKGROUND AND AIM: This study used Long Short-Term Memory (LSTM) neural networks to develop a predictive model for PM10 and Aerosol Optical Depth (AOD) concentrations in the city of Agadir, Morocco. METHOD: Meteorological conditions other atmospheric compositions were as predictors. RESULTS: The LSTM outperformed traditional statistical models by accurately forecasting AOD concentrations. CONCLUSIONS: demonstrated effectiveness particulate dust emphasized importance including relevant...
This paper presents a tele-expertise experiment in dermatology with the aim of early detection skin cancers. Thistelemedicine system allows general practitioner (GP) to send request for an opinion distant dermatologist. Carriedout