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Background: An increasing amount of health education resources for patients and professionals are distributed via social media channels. For example, thousands of health education videos are disseminated via YouTube. Often, tags are assigned by the disseminator. However, the lack of use of standardized terminologies in those tags and the presence of misleading videos make it particularly hard to retrieve relevant videos., Objectives: i) Identify the use of standardized medical thesauri (SNOMED CT) in YouTube Health videos tags from preselected YouTube Channels and demonstrate an information technology (IT) architecture for treating the tags of these health (video) resources. ii) Investigate the relative percentage of the tags used that relate to SNOMED CT terms. As such resources may play a key role in educating professionals and patients, the use of standardized vocabularies may facilitate the sharing of such resources. iii) Demonstrate how such resources may be properly exploited within the new generation of semantically enriched content or learning management systems that allow for knowledge expansion through the use of linked medical data and numerous literature resources also described through the same vocabularies., Methods: We implemented a video portal integrating videos from 500 US Hospital channels. The portal integrated 4,307 YouTube videos regarding surgery as described by 64,367 tags. BioPortal REST services were used within our portal to match SNOMED CT terms with YouTube tags by both exact match and non-exact match. The whole architecture was complemented with a mechanism to enrich the retrieved video resources with other educational material residing in other repositories by following contemporary semantic web advances, in the form of Linked Open Data (LOD) principles., Results: The average percentage of YouTube tags that were expressed using SNOMED CT terms was about 22.5%, while one third of YouTube tags per video contained a SNOMED CT term in a loose search; this analogy became one tenth in the case of exact match. Retrieved videos were then linked further to other resources by using LOD compliant systems. Such results were exemplified in the case of systems and technologies used in the mEducator EC funded project., Conclusion: YouTube Health videos can be searched for and retrieved using SNOMED CT terms with a high possibility of identifying health videos that users want based on their search criteria. Despite the fact that tagging of this information with SNOMED CT terms may vary, its availability and linked data capacity opens the door to new studies for personalized retrieval of content and linking with other knowledge through linked medical data and semantic advances in (learning) content management systems.

Health information retrieval and YouTube can be used as powerful tools to improve user's health knowledge. However, YouTube videos must be carefully analysed in order to avoid misleading, inaccurate, obsolete and incorrect health content. We present an approach for re-ranking health videos obtained from YouTube, called Domain-based ranking. Our system automatically identifies videos coming from trusted sources (channels), such as hospitals and health organizations, and re-ranks YouTube results so that such videos are presented first in the ranking list. Video and channel metadata are used to automatically determine if a video is provided by a trusted source. The approach is tested and results show that the amount of relevant and reliable videos ranked within top-10 increase when using Domain-based ranking, compared with the original YouTube ranking.

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization.

Health consumers have embraced the web to obtain access to health information and to socialize and share knowledge with peers. Additionally, the web has become a more interactive and rich platform with the integration of health applications and services, such as Personal Health Records. Some of these applications provide personalized interactions based on user specific characteristics. In this paper we provide an overview of Personalized Health Applications in the Web 2.0. We reviewed the health applications integrated in Google Health, Microsoft HealthVault and Facebook. We studied the goals of the applications and also the personalized feedback they provided.

The use of computers in health education started more than a decade ago, mainly for tailoring health educational resources. Nowadays, many of the computer-tailoring health education systems are using the Internet for delivering different types of health education. Traditionally, these systems are designed for a specific health problem, with a predefined library of educational resources. These systems do not take advantage of the increasing amount of educational resources available on the Internet. One of the reasons is that the high availability of content is making it more difficult to find the relevant one. The problem of information overload has been addressed for many years in the field of recommender systems. This paper is focused on the challenges and opportunities of merging recommender systems with personalized health education. It also discusses the usage of social networks and semantic technologies within this approach.