Efficient Context Prediction for Decision Making in Pervasive Health Care Environments: A Case Study

摘要: Mobile real-time decision support systems (RTDSS) find themselves deployed in a highly dynamic environment. Decision makers must be assisted, ­taking into account the various time-critical requirements. Perhaps even more important is fact that quality of given by system depends heavily on knowledge current and future contexts system. A DSS should exhibit inherent proactive behaviour automatically derive ­decision-making person (DMP)’s needs for specific information from context surrounds him/her. We propose to run top middleware helps maker contextualise information. Moreover, we give set requirements fulfil learn, detect, predict patterns optimise flow maker. The approach made concrete validated case study domain medical health care. Representative location prediction algorithms are evaluated using an existing dataset.