Decision support system for medical-social healthcare
The research is being carried out in close collaboration with a French mutual health benefit organization called "Mutualité Française de la Loire". This is a non-profit making organization which provides health and social services care in France. Catherine Combes.
The research was carried out in close collaboration with the French co-operative health organization called the “Centre Mutualiste d’Addictologie”, an aftercare center for addictology. This work is in close collaboration with the Dr. Christian Digonnet, psychiatrist and Manager of the addiction Center - Saint-Galmier France (2009-2011).
The research investigates clustering techniques. The work deals with the identification of patient’s profiles. The machine learning tools are called "unsupervised" learning. The objective is to propose a data mining approach in order to automatically identify patterns (on the co-occurrence concepts of observed variables). Psychiatric disorders related to addictions are studied in a population-based sample to determine whether conditions co-occur that is the recognition of homogenous groups of patients based on their features in such a way that the patients belonging to the same groups are similar and those belonging to different groups are dissimilar. The aim is automatically to find the hidden structure corresponding to feature-patterns related to people suffering from addictions. We want to automatically find the number of categories or groups of patients and the “best” representative patients’ profile of each group. We also present the specificity of the used distance in order to describe of how far apart objects are.
This work is in close collaboration with nursing homes. It concerns the degree of self-handicap for the elderly dependent people living in nursing home (2006-2010).
We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). The aim is to forecast the residents’ autonomy/disability progress over time (in using techniques as grammar inference to identify the transition graph between profiles) in order to plan the activities and the necessary human, material and financial resources.