=Paper=
{{Paper
|id=Vol-485/paper-16
|storemode=property
|title=MyHealthEducator: Personalization in the Age of Health 2.0
|pdfUrl=https://ceur-ws.org/Vol-485/paper3-S.pdf
|volume=Vol-485
|dblpUrl=https://dblp.org/rec/conf/um/Fernandez-Luque09
}}
==MyHealthEducator: Personalization in the Age of Health 2.0==
https://ceur-ws.org/Vol-485/paper3-S.pdf
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
MyHealthEducator: Personalization in the Age
of Health 2.0
Luis Fernandez-Luque
Northern Research Institute, Tromso, Norway,
luis.luque@norut.no
Abstract. Most Europeans use the Internet for searching health infor-
mation [1] and many of them use the Web 2.0 to access health information
and services, share knowledge and socialize. There is an emerging trend
towards the developing of personalized Health 2.0 applications which
could dramatically change how the health consumers use the Web. This
paper provides an overview of personalization in the Health 2.0 domain
and it presents the ongoing project MyHealthEducator, which is an early
example of personalization in the Age of Health 2.0. MyHealthEducator
aims to study the feasibility of using Recommender Technologies for
delivering personalized and adaptive recommendations of web health in-
formation based on the user’s Personal Health Records and content from
their community (e.g. user’s comments).
Key words: eHealth, Personalization, Health 2.0, Health Education
1 Introduction
Personalization is not new in eHealth, especially in health education[2, 3]. It has
been traditionally based on explicit feedback (e.g. questionnaires) for delivering
tailored educational resources aiming at modifying a health behavior (e.g. stop
smoking). In general, these stand-alone systems are static and designed for a
specific disease, taking into account a closed set of parameters and resources
controlled by healthcare professionals. This approach is not aimed at the cur-
rent context of the Web 2.0, where many different types of health resources
are appearing. For example, health consumers are creating content (e.g. blogs,
v-logs, comments) and socializing through Social Networks (e.g. Facebook, Tu-
diabetes.com). They are also managing their health records by using web-based
Personal Health Records (PHRs) such as Google Health.
The Web 2.0 provides many opportunities for personalized health applica-
tions, especially due to the increased availability of information about the users.
For example, approximately half of teenagers’ profiles in MySpace contain private
health information (e.g. drug abuse, sexuality, etc.) [4]. This type of information
is being used in the project Riskbot [5] for delivering personalized health pro-
motion messages. Tags [6] and ratings [7] have been also used in personalized
health education. In addition, there are already personalized applications based
on the data available in Google’s and Microsoft’s PHRs. Bourgeois et al. [8] used
Indivo PHR [9] for delivering tailored messages about influenza vaccination.
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
2 MyHealthEducator
MyHealthEducator approach consists of a service for recommending personal-
ized health information adapted to the changing needs of the patients and not
designed for a specific disease. Its main characteristic is the adaptability to the
changes, both in the educational resources and in the user’s data. We are aiming
to achieve this adaptability with Semantic Modeling techniques to create dy-
namic models of the users and educational resources. The knowledge about the
health user’s status, preferences, and demographic information will be modeled
as the changing user’s context (e.g. diagnosed diseases) and gathered mainly
from their PHRs. MyHealthEducator, figure 1, comprises 3 main components:
1) the User-models Repository which contains the information about the users
2) the Health-Repository with the metadata about the educational resources
and 3) the Recommender Engine. The system is integrated with external com-
Fig. 1. Overview of MyHealthEducator
ponents, such as the PHRs, repositories of health educational resources and the
user interface. The user interface will vary depending on the platform where it
is integrated. Currently, it is being designed to be integrated as a web-based
gadget in our telemedicine platform MyHealthService [10].
2.1 User-models Repository and Health Repository
The User-models Repository contains the information about the users. The
health information will be gathered from the PHRs. After the users grant access
to their PHRs, the system can access the user’s data using the PHR’s secure
APIs. The health information will be modeled as context, which could vary and
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
be different between the users. The non-health related information about the
users is also modeled and stored in a Personal Record containing information
such as the user’s preferences, which will be gathered using implicit feedback
(e.g. user’s interaction with the system) and explicit feedback (e.g. favorited
content provider). The models will be based on Semantic Technologies, such as
Semantic Networks or Concept Profiles. Instead of extracting list keywords to
build the user models the system captures linked concepts and terms, decreasing
the polysemy problem. We are looking into the usage of the Unified Medical
Language System (UMLS) Semantic Networks, which are widely used in the
health domain and have been ported to OWL.
The Health Repository contains the metadata about the web-based edu-
cational resources. Similar to the User-models the available information about
the resources (e.g. descriptions, comments) will be analyzed to extract relevant
keywords and concepts in order to build a semantic model of the resources. The
information created by the community of users (e.g. ratings, comments) will be
also used to enrich the resource’s model. One of the main challenges to address
will be the diversity of the users’ vocabulary and the use of acronyms.
2.2 Recommender Engine
The recommender engine will be a hybrid Recommender System based on: 1)
the analysis of the semantic structure of the models about the users and the edu-
cational resources, 2) collaborative techniques. A pre-filtered list of educational
resources is generated by analyzing the semantic similarity between the users
and resources models. Finally, the list is sorted using collaborative techniques.
2.3 Status and future work
MyHealthEducator is currently under development based on our previous studies
about the Patient Generated Content, such as educational resources [11] and
comments [12]. The first prototype, which is expected by the end of 2009, will
be a recommender system of health videos from YouTube based on the analysis
of the User Generated Content and the PHRs. The evaluation of this prototype
will be focused on the evaluation of different recommendation algorithms based
on the analysis of data collected from the system usage and users’ feedback (e.g.
surveys).
3 Conclusions
The increased availability of structured and un-structured data about health
consumers and content has opened a new conduit for research opportunities to-
wards the development of personalized Health 2.0 applications, where PHRs are
becoming platforms with ecosystems of personalized health applications. The
impact of these applications can ultimately lead to a paradigm shift of patient-
centered healthcare systems. Many challenges are also appearing; including new
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
ethical dilemmas related to web-mining sensitive information or technical ques-
tions regarding to the interoperability and integration. Some of these challenges
are being addressed in the ongoing project MyHealthEducator. This project will
increase the knowledge about the usage of Web Technologies for health person-
alization.
4 Acknowledgements
I would like to thank MyHealthService team, especially Randi Karlsen and Lars
K. Vognild. This project belongs to the Tromso Telemedicicine Laboratory (co-
funded by the Research Council of Norway).
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