Predictive Data Mining to Learn Health Vitals of a Resident in a Smart Home

This paper addresses multi-source information integration in stochastic environments where information from sources consists of probabilistic domain models (represented as joint distributions or Bayesian networks) learned from data. We extend the batch algorithm proposed by Maynard-Reid IIand Chajewska (2001) to accommodate incremental integration so as to support ´anytime´ querying. Experimental results verify that our algorithms compare well with the batch algorithm in accuracy and efficiency. Extensions for integrating joint distributions are independent of the order in which sources arrive, but the Bayesian network integration extension is only approximately so. This is due to bias introduced by the algorithm´s use of heuristic optimization, and an ´inertial´ effect that makes this bias difficult to undo over time.