Recommender Systems for Health Education
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Recommender Systems for Health Education

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Presentation at MIE 2009 about the use of recommender systems for health education

Presentation at MIE 2009 about the use of recommender systems for health education

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    Recommender Systems for Health Education Recommender Systems for Health Education Presentation Transcript

    • Introduction Recommender Systems for Health Conclusions Challenges and Opportunities of using Recommender Systems for Personalized Health Education Luis Fernandez-Luque*1 Randi Karlsen12 Lars K. Vognild1 1 Northern Research Institute (Norut), Tromso, Norway 2 Computer Science Department, University of Tromso, Tromso, Norway Medical Informatics Europe, MIE 2009, 2nd September 2009
    • Introduction Recommender Systems for Health Conclusions INTRODUCTION Health Education Health Education Education that increases the awareness and favorably influences the attitudes and knowledge relating to the improvement of health on a personal or community basis. (WHO) Tailored Health Education The adaptation of health education to one specific person through a largely computerized process a a Hein de Vries et al., Computer-tailored interventions motivating people to adopt health promoting behaviors: Introduction to a new approach, Patient Education and Counseling
    • Introduction Recommender Systems for Health Conclusions INTRODUCTION Tailored Health Education Designed for a specific disease or attitude, based on human experts and theoretical models.1 Tailoring/Personalization has three main elements: User modeling: gathering patient information (e.g. standardized questionnaires or EHR) Document modeling: description of educational resources (e.g. manual techniques) Tailoring algorithm: selection and modification of resources, based on expert rules Channel: delivery of tailored resources (e.g. email, post, web, SMS, etc.) 1 Lustria ML et al., Computer-tailored health interventions delivered over the Web: review and analysis of key components., Patient Education and Counseling
    • Introduction Recommender Systems for Health Conclusions INTRODUCTION Health Education in the Web 2.0 Most of the population uses the Internet to access health information Many types of resources: videos, blogs, images, forums, flash-tutorials, etc. Published by hospitals, medical associations, patients, government, etc. Also, it is difficult to find good content: autopsy pictures, herbal cures for cancer, etc.
    • Introduction Recommender Systems for Health Conclusions INTRODUCTION Information Overload The web search space is so huge (Information Overload) that Information Filtering techniques are needed, such as Search Engines (e.g. Google) and Recommender Systems (e.g. Amazon Recommendations).
    • Introduction Recommender Systems for Health Conclusions INTRODUCTION Information Overload The web search space is so huge (Information Overload) that Information Filtering techniques are needed, such as Search Engines (e.g. Google) and Recommender Systems (e.g. Amazon Recommendations).
    • Introduction Recommender Systems for Health Conclusions RECOMMENDER SYSTEMS What is a Recommender System? Recommender System Recommender systems form a specific type of Information Filtering (IF) technique that attempts to present information items (e.g. movies, music, books, news, images, web pages, etc.) that are likely of interest to the user. (Wikipedia)
    • Introduction Recommender Systems for Health Conclusions RECOMMENDER SYSTEMS Main types of Recommender Systems There are three main types of Recommender Systems: Collaborative: based on knowledge gathered from users Content: based on knowledge gathered from the users and item descriptions Hybrid: a combination of different techniques Collaborative Recommender System 1 User modeling: previous interactions and user ratings 2 Doc modeling: the collection of ratings from different users 3 Algorithm: recommendations are based on data collected from a particular user’s neighborhood (e.g. "people like you like") Problems and advantages: there is no need to describe items (+) and recommendations are not repetitive (+), yet performance is low with new users and items (e.g. cold start problem) (-)
    • Introduction Recommender Systems for Health Conclusions RECOMMENDER SYSTEMS Main types of Recommender Systems There are three main types of Recommender Systems: Collaborative: based on knowledge gathered from users Content: based on knowledge gathered from the users and item descriptions Hybrid: a combination of different techniques Content-based Recommender System 1 User modeling: previous interactions and user ratings 2 Doc modeling: item characteristics. 3 Algorithm: recommendations are based on data collected from previous user interactions (e.g. "these items are similar to what you liked before") Problems and advantages: items need to be described (-), recommendations can be repetitive (-), there is no need to have a critical mass of users (+), low performance with new users (-)
    • Introduction Recommender Systems for Health Conclusions EXAMPLES Examples of Recommender Systems for Health HealthyHarlem: tag-based recommender system that suggests online resources in a health promotion community (Khan SA, University of Columbia, USA) Cancer Sites Recommender: usage of collaborative and content-based techniques to recommend prostate cancer webs (Witteman H, University of Toronto, Canada) Suggestion systems for educational resources while navigating patient records. MyHealthEducator: an ongoing project where video recommendations are based on collaborative techniques and a Personal Health Record (L. Fernandez-Luque, Norut, Norway)
    • Introduction Recommender Systems for Health Conclusions CHALLENGES AND OPPORTUNITIES Recommender Systems for Health Education: Opportunities Recommender Systems need less expert involvement due to automatic and collaborative techniques. Integration with Personal Health Records (PHR) can improve recommendations and reduce the cold start problem. Collaborative techniques gather aspects such as user preferences, which are not very common in health education tailoring. Automatic analysis of User-Generated Content for modeling users (e.g. Risbot) or modeling documents (e.g. HealthyHarlem) can improve recommendations and increase knowledge about the users The knowledge of the human experts about tailoring health education can improve health recommender systems.
    • Introduction Recommender Systems for Health Conclusions CHALLENGES AND OPPORTUNITIES Recommender Systems for Health Education: Challenges Recommender Systems can be attacked by users (e.g. to promote a certain document) Most Recommender Systems are based on popularity and thus may not lead to good resources (e.g. proanorexia videos are popular) Integration between web health applications is not yet prominent Web data mining for user modeling has ethical implications (e.g. should we model race, gender, sexual orientation?)
    • Introduction Recommender Systems for Health Conclusions CONCLUSIONS Conclusions It is difficult to find web educational resources due to Information Overload. Recommender Systems have the potential to facilitate access to relevant educational resources since they are designed for the context of Information Overload. Health Education differs a lot from the traditional scenarios of Recommender Systems A lot of user information (if integrated with a PHR) Health Education can not be only based on popularity. Resources need to be quality controlled.
    • Introduction Recommender Systems for Health Conclusions CONCLUSIONS Thank you Luis Fernandez-Luque (luis.luque@norut.no)