  Falls are a common problem affecting the older adults and a major public
health issue. Centers for Disease Control and Prevention, and World Health
Organization report that one in three adults over the age of 65 and half of the
adults over 80 fall each year. In recent years, an ever-increasing range of
applications have been developed to help deliver more effective falls
prevention interventions. All these applications rely on a huge elderly
personal database collected from hospitals, mutual health, and other
organizations in caring for elderly. The information describing an elderly is
continually evolving and may become obsolete at a given moment and contradict
what we already know on the same person. So, it needs to be continuously
checked and updated in order to restore the database consistency and then
provide better service. This paper provides an outline of an Obsolete personal
Information Update System (OIUS) designed in the context of the elderly-fall
prevention project. Our OIUS aims to control and update in real-time the
information acquired about each older adult, provide on-demand consistent
information and supply tailored interventions to caregivers and fall-risk
patients. The approach outlined for this purpose is based on a polynomial-time
algorithm build on top of a causal Bayesian network representing the elderly
data. The result is given as a recommendation tree with some accuracy level. We
conduct a thorough empirical study for such a model on an elderly personal
information base. Experiments confirm the viability and effectiveness of our
OIUS.
