A graph based recommender system for managing Covid-19 Crisis

The paper aims to present a graph based recommender system for managing the Covid-19 crisis by considering patient and medical staff data. Working with limited number of medical staff, require optimization when creating the appropriate medical staff to assist patient. Patient medical files usually contain more information about the patient diseases and symptoms. In this paper the recommender system at first analyses the patient medical files to find and decide which profile of medical staff could assist efficiency this patient in a crisis situation. Second the recommender system by taking into account the availability of the medical staff will try to propose others doctors with the same profile and the nearest competencies.

Managing COVID-19 Crisis using C3HIS Ontology.

The paper aims to present the C3HIS Ontology project, a web based solution for Covid-19 Crisis Health Care Information System. In the health care services, employee skills are a major resource and an essential part of everyday practice and a requirement for all health professions. We aim to prove how using individual profiles based on competencies can make a difference between life and death in times. As the performance assessment is driven by actors competencies we have to put human actors in the core of quality processes of health care services management in COVID-19 crisis.