2025

Conférence

Paper Conference

Home Health Care (HHC) delivers medical and personal
care in patients’ homes, offering an alternative to hospital
treatment. Growing demand due to aging populations and
chronic diseases creates challenges in caregiver assignment,
scheduling, and demand forecasting, leading to higher costs,
caregiver burnout, and reduced patient satisfaction.
To address these challenges, advanced computational
approaches are increasingly explored. In particular, the
integration of Machine Learning (ML) and Optimization
(OPT) enables adaptive, data-driven solutions. This article
explores traditional, metaheuristic, and hybrid methods
to improve efficiency, analyzing current methodologies,
challenges, limitations, and future research directions.

@inproceedings{massaoudi2025leveraging,
  author    = {Massaoudi, Ghofran and Chaabani, Abir},
  title     = {Leveraging Machine Learning and Optimization in Home Health Care: Emerging Trends and Future Opportunities},
  booktitle = {2025 11th International Conference on Control, Decision and Information Technologies (CoDIT)},
  year      = {2025},
  doi       = {10.1109/CoDIT66093.2025.11321598},
  publisher = {IEEE}
}