Introduction MyHealthEducator Conclusions

MyHealthEducatorPersonalization in the Age of Health 2.0

Luis Fernandez-Luque

Northern Research Institute, Tromso (Norway)

Adaptation and Personalization for Web 2.0, UMAP 2009, June2009

Introduction MyHealthEducator Conclusions

INTRODUCTION

Health Information and Personalization

Internet Health InformationMost of the Europeans use Internet to find health information.The Information Overload is growing (size and complexity).There are many initiatives to certify webs (e.g. HON).Resources can be both very good and dangerous.

Tailored Health EducationDesigned for specific health problems, mainly messages tomodify behaviors.Techniques: rule-base, questionnaires, predefined set ofresources, NLP, etc.

Introduction MyHealthEducator Conclusions

INTRODUCTION

The e-Patients in the Web 2.0

1 Socializing/Virtual Communities2 Creating and sharing information3 Accesing e-Health Services: such as Personal Health Records

(PHR) for controlling our personal information and an ecosystemof eHealth applications

Introduction MyHealthEducator Conclusions

MYHEALTHEDUCATOR

MyHealthEducator

What is MyHealthEducator?

It is a PhD Project: An adaptive Recommender System of healtheducation based on heterogeneous and changing data sources

A tool to assist the users to find relevant resources

Adaptable to changes in the user’s health

Enabling technologies1 Semantic-enhanced dynamic modeling2 Context-aware recommender techniques: user’s health is part of the

changing user context3 Others: Collaborative Techniques, Personal Health Records, analysis of

User Generated Content, Consumer Health Vocabulary, etc.

Introduction MyHealthEducator Conclusions

MYHEALTHEDUCATOR

Overview

Introduction MyHealthEducator Conclusions

MYHEALTHEDUCATOR

Health Repository

Introduction MyHealthEducator Conclusions

MYHEALTHEDUCATOR

User Modeling

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MYHEALTHEDUCATOR

Recommender Engine and Evaluation

It is a hybrid Recommender System based on:Context-aware: semantic similarity to match videos with patient’shealth contextCollaborative filtering: highly rated videos by similar users (e.g.health, age, gender)

Different configurations will be tested (prefiltering with collaborativetechniques, weighted combination, etc.) in real setting:

1 Beta application (web-gadget) application installable in PHRs2 Integrated and tested in ongoing research projects (e.g. Tromso

Telemedicine Lab projects, European Projects)3 Evaluation will be focused on the technical validation, not a clinical

study.

Introduction MyHealthEducator Conclusions

CONCLUSIONS

Conclusions

Work done so farStudy of the literature: Personalization and Medicine 2.0,Tailored health education, Recommender Systems (accepted toMIE 2009)Study of User Generated Content: survey to e-Patients, analysisYouTube comments in health videos (presented at Medicine 2.02008, accepted to MIE 2009)Design of the system and prototype for crawling videos.

Future WorkDesign and first prototypes by the end of summer 2009Deployment and evaluation in 2010

Introduction MyHealthEducator Conclusions

CONCLUSIONS

Thanks for your attention

Luis Fernandez (luis.luque@norut.no)