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In recent years, as the big data boom accelerates, the possibility of using personal health and medical data is also growing. However, since most of previous studies have focused on computerizing, storing, and transferring of medical data, it is hard to say that they intelligently use medical data. Particularly, in cases of urgent diseases like acute myocardial infarction (AMI) that prompt diagnosis and treatment is needed, the current hospital information systems are difficult to efficiently provide information. Therefore, in this paper, we propose a convergence modeling method based on semantic relations by analyzing characteristics of medical data for AMI. The proposed method can unify medical data which is separately stored in medical information systems and provide important data as a one record.
Meeyeon Lee; Ye-Seul Park; Myung-Hee Kim; Jung-Won Lee. Convergence Modeling of Heterogeneous Medical Information for Acute Myocardial Infarction. Lecture Notes in Electrical Engineering 2015, 757 -763.
AMA StyleMeeyeon Lee, Ye-Seul Park, Myung-Hee Kim, Jung-Won Lee. Convergence Modeling of Heterogeneous Medical Information for Acute Myocardial Infarction. Lecture Notes in Electrical Engineering. 2015; ():757-763.
Chicago/Turabian StyleMeeyeon Lee; Ye-Seul Park; Myung-Hee Kim; Jung-Won Lee. 2015. "Convergence Modeling of Heterogeneous Medical Information for Acute Myocardial Infarction." Lecture Notes in Electrical Engineering , no. : 757-763.
In recent years, a large portion of smartphone applications (Apps) has targeted context-aware services. They aim to perceive users’ real-time context like his/her location, actions, or even emotion, and to provide various customized services based on the inferred context. However, context-awareness in mobile environments has some challenging issues due to limitations of devices themselves. Limited power is regarded as the most critical problem in context-awareness on smartphones. Many studies have tried to develop low-power methods, but most of them have focused on the power consumption of H/W modules of smartphones such as CPU and LCD. Only a few research papers have recently started to present some S/W-based approaches to improve the power consumption. That is, previous works did not consider energy consumed by context-awareness of Apps. Therefore, in this paper, we focus on the power consumption of context-aware Apps. We analyze the characteristics of context-aware Apps in a perspective of the power consumption, and then define two main factors which significantly influence the power consumption: a sort of context that context-aware Apps require for their services and a type of ways that a user uses them. The experimental result shows the reasonability and the possibility to develop low-power methods based on our analysis. That is, our analysis presented in this paper will be a foundation for energy-efficient context-aware services in mobile environments.
Meeyeon Lee; Deok-Ki Kim; Jung-Won Lee. Analysis of Characteristics of Power Consumption for Context-Aware Mobile Applications. Information 2014, 5, 612 -621.
AMA StyleMeeyeon Lee, Deok-Ki Kim, Jung-Won Lee. Analysis of Characteristics of Power Consumption for Context-Aware Mobile Applications. Information. 2014; 5 (4):612-621.
Chicago/Turabian StyleMeeyeon Lee; Deok-Ki Kim; Jung-Won Lee. 2014. "Analysis of Characteristics of Power Consumption for Context-Aware Mobile Applications." Information 5, no. 4: 612-621.