A smart health service model for elders based on ECA-S rules

Health services delivery relating to chronic diseases in elders are becoming a hot spot with the progressively severity in population aging in China. To provide a better service system for the elders, researchers have used active rule based techniques including Event-Condition-Active (ECA). In this paper, we enhanced ECA to ECA-Sequence (ECA-S) rules to detect abnormal condition in physiological indices. Our method extracts accurate health information from historical sequence data. The ECA-S is used to differentiate burst variations in physiological indices from morbid cases. Our further experiment in giving early warning for diabetic patients shows that ECA-S can deal with the burst abnormal physiological index problem. Finally, an evaluation is made among existing ECA based solutions including prime ECA, ECA-Parameters, ECA-Post-condition and proved that our proposed ECA-S method performs better in health diagnosis quality as compared to others.