Chronic disease may lead to life threatening health complications like heart disease, stroke, and diabetes that diminish the quality of life. CDSS (Clinical Decision Support System) helps physician in effective utilization of patient’s clinical information at the time of diagnosis and medication. This paper points out the importance of social media and interaction integration in existing Smart CDSS for chronic diseases. The proposed system monitors health conditions, emotions and interests of patients from patients’ tweets, trajectory and email analysis. We extract keywords, concepts and sentiments from patient’s tweets data. Trajectory analysis identifies the focused activities after considering imperative location and semantic tags. Email analysis finds interesting patterns and communication trends from daily routine of patient. All these outputs are supplied to Smart CDSS into vMR (virtual Medical Record) format through social media adapter. This helps the health practitioners to understand the behavior and lifestyle of patients for better decision making about treatment. Consequently, patients can get continuous relevant recommendations from Smart CDSS based on their personalized profile. To verify and validate the working of proposed methodology, we have implemented a proof of concept prototype that reflects its complete working with potential outcomes.
Bibliographical noteFunding Information:
This research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2013-(H0301-13-2001))
© 2013, Springer Science+Business Media New York.
Copyright 2015 Elsevier B.V., All rights reserved.
- Social media
ASJC Scopus subject areas
- Hardware and Architecture