Designing a Mobile Recommender System for Treatment Adherence Improvement among Hypertensives

Impelling the ambulatory hypertensive patients to stick to the prescribed treatment throughout a long term is a challenging problem. To address the problem, the personal monitoring system can be used providing the possibility both to gather various health state parameters and life style-related data and to intervene in case the patient does not stick to the appointed instructions. The subsystem related to health state monitoring have been presented in our previous work. In this paper, we introduce the recommender system intended to patient's behavior correction

Multi-Source Data Sensing in Mobile Personalized Healthcare Systems: Semantic Linking and Data Mining

The paper introduces an approach to collecting and mining health-related information on the patient based on sensed data from various medical devices as well as from other digitallyenabled sources. The regularly sensed data are semantically linked thus creating an additional information space-semantic layer. On the latter, a linked knowledge-rich structure-semantic network-is maintained and used to construct mobile services. The use of various medical devices and other data sources makes it possible to remotely monitor patients' vital physiological parameters and other important health-related events. It includes sensing the context of the physical environment, which is then coupled with the health state of the patient. Several patients and interested people can be virtually integrated into a group. Consequently, social methods can be used for enhancing the treatment adherence and for motivating the healthcare goals. As such, the healthcare services would have became more focused on the patients and their needs. Ultimately, the mobile healthcare moves towards the vision of At-Home Laboratory (AHL) that diminishes the necessity to visit the hospital and to directly use its facility