Publications by authors named "Iyabosola Busola Oronti"

Article Synopsis
  • Ensemble tree-based models, particularly the BME model developed for cardiovascular outcomes, showed promise in handling correlated hospital-level data better than traditional methods.
  • The study involved a large dataset from 42 UK hospitals, focusing on predicting 30-day mortality rates for cardiac surgery patients, to compare the performance of various modeling techniques.
  • Results indicated that while the BME model had superior prediction power at smaller sample sizes, traditional Xgboost models performed better with larger datasets, suggesting mixed effects can improve machine learning applications in healthcare.
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Introduction: This article aims at investigating social engagement in the fight against the COVID-19 pandemic in low-resource settings (LRSs). In particular, it focuses on Benin (Sub-Saharan Africa), and reports the results of a field study that investigated the local people's acceptance of the vaccine and the tracking program.

Methods: This project is the product of a collaboration between the ABSPIE (Applied Biomedical and Signal Processing E-Health) Lab of the University of Warwick (UK) and the LAMA (Laboratoire d'Antropologie Medical Appliqué) of the University of Abomey Calavi (Benin).

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