Social health data integration using semantic Web
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There is a large amount of free health information available for a patient to address her health concerns. HealthData.gov includes community health datasets at the national, state and community level, readily downloadable. There are also patient-generated datasets, accessible through social media, on the conditions, treatments or side effects that individual patients experience. While caring for patients, clinicians or healthcare providers may benefit from integrated information and knowledge embedded in the open health datasets, such as national health trends and social health trends from patient-generated healthcare experiences. However, the open health datasets are distributed and vary from structured to highly unstructured. An information seeker has to spend time visiting many, possibly irrelevant, websites, and has to select relevant information from each and integrate it into a coherent mental model. In this paper, we present a Linked Data approach to integrating these health data sources and presenting contextually relevant information called Social InfoButtons to healthcare professionals and patients. We present methods of data extraction, and semantic linked data integration and visualization. A Social InfoButtons prototype system provides awareness of community and patient health issues and healthcare trends that may shed light on patient care and health policy decisions.