A Semantic Model to Address Health Questions to Professionals in Healthcare Social Networks

A Semantic Model to Address Health Questions to Professionals in Healthcare Social Networks

Francisco Echarte (Universidad Pública de Navarra, Spain), José Javier Astrain (Universidad Pública de Navarra, Spain), Alberto Córdoba (Universidad Pública de Navarra, Spain) and Jesús Villadangos (Universidad Pública de Navarra, Spain)
DOI: 10.4018/978-1-61520-777-0.ch005
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Internet social networks offer a wide variety of possibilities, including communication between users, sharing information, and the creation of virtual communities on many different subjects. One of these subjects is healthcare, where different social networks are now appearing and covering different objectives. In this chapter, a social network is described, where users can formulate healthcare questions that are automatically classified under concepts of a medical ontology and assigned to experts of each topic. These questions are then answered by healthcare expert physicians. This chapter includes a semantic classifying method that provides the automatic classification of questions by means of a medical ontology, based on the tags used to annotate them, and the previously classified questions. The chapter includes an ontological model that represents the questions, the assigned tags, the answers, the physicians, and the medical concepts.
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Nowadays, we assist to the emergence of different social networks based on some of the features offered by Web 2.0 technologies. Users of social networks are the main actors in this kind of webs: creating new content that are accessible to other users of the Web, interacting with other users, creating relationships, and defining, in a great extent, the evolution of theses networks.

Healthcare social networks (HSNs) provide new social and commercial possibilities. They provide new communication and interaction channels among patients and physicians, they provide new healthcare services, and offer new business trends.

There exist many ways to apply social networks to the healthcare field:

  • i)

    Patients sharing their own treatment experiences with other patients (PatientsLikeMe1).

  • ii)

    Physicians retrieving knowledge by reading medical literature and interacting with peers (Sermo2, PeerClip3, Within34).

  • iii)

    Medical students, residents improving their knowledge (knowledge discovery and collective wisdom) (SocialMD5)

  • iv)

    Online support groups (MDJunction6);

  • v)

    Rating physicians (RateMDs7).

  • vi)

    Physicians and patients sharing experience both together (WegoHealth8). HSNs concern patients (iMedix9), physicians (Sermo, PeerClip, Within, SocialMD, Ozmosis10), students (SocialMD, Tiromed11, DOctorsHangout12) and nurses (Nursing World13, Nurselinkup14).

From the physician point of view, healthcare information is hierarchical and formally well classified by means of ontologies. Healthcare terminologies like SNOMED15, openGalen16, MeSH17, UMLS18 or ICD19 are used in healthcare environments for different purposes as clinical history encoding, statistical analysis of medical activities and procedures, etc. These terminologies are often lengthy and complicated to use even for healthcare professionals, and of course very complex and often unintelligible to be used by common users of social networks without healthcare knowledge. In this sense, folksonomies (Vander Val, 2007) provide an easier way to create, browse and search information since they are less restrictive and rigid than the medical terminologies.

One of the more interesting features of this kind of social networks is that users can address questions concerning their health directly to medical specialists, and also to patients with similar pathologies (a way to share experiences). The main problem is the difficulty addressed to question classification. Users classify healthcare questions easily by means of tags, but this produces fuzziness in the classification. The classification of tags using folksonomies is less strict than that obtained with other more formal methods like ontologies. Therefore, there not exists any kind of general classification criteria. Users tag their questions following their own medical knowledge (often limited). This makes navigation among the questions difficult, since other users (i.e. medical specialists) must browse and search the questions using the limited capabilities of folksonomies.

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