recommender systems for health education
DESCRIPTION
Presentation at MIE 2009 about the use of recommender systems for health educationTRANSCRIPT
Introduction Recommender Systems for Health Conclusions
Challenges and Opportunities of usingRecommender Systems for Personalized
Health Education
Luis Fernandez-Luque*1 Randi Karlsen12 Lars K. Vognild1
1Northern Research Institute (Norut), Tromso, Norway
2Computer 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 EducationEducation that increases the awareness and favorably influencesthe attitudes and knowledge relating to the improvement ofhealth on a personal or community basis. (WHO)
Tailored Health EducationThe adaptation of health education to one specific personthrough a largely computerized process a
aHein de Vries et al., Computer-tailored interventions motivating people to adopt healthpromoting 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 humanexperts and theoretical models.1Tailoring/Personalization has three main elements:
User modeling: gathering patient information (e.g. standardizedquestionnaires or EHR)Document modeling: description of educational resources (e.g.manual techniques)Tailoring algorithm: selection and modification of resources,based on expert rulesChannel: delivery of tailored resources (e.g. email, post, web,SMS, etc.)
1Lustria ML et al., Computer-tailored health interventions delivered over the Web: review andanalysis 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 healthinformationMany 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, herbalcures for cancer, etc.
Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Information Overload
The web search space is so huge (Information Overload) thatInformation Filtering techniques are needed, such as Search Engines(e.g. Google) and Recommender Systems (e.g. AmazonRecommendations).
Introduction Recommender Systems for Health Conclusions
INTRODUCTION
Information Overload
The web search space is so huge (Information Overload) thatInformation Filtering techniques are needed, such as Search Engines(e.g. Google) and Recommender Systems (e.g. AmazonRecommendations).
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 likelyof 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 usersContent: based on knowledge gathered from the users and itemdescriptionsHybrid: a combination of different techniques
Collaborative Recommender System1 User modeling: previous interactions and user ratings2 Doc modeling: the collection of ratings from different users3 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 lowwith 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 usersContent: based on knowledge gathered from the users and itemdescriptionsHybrid: a combination of different techniques
Content-based Recommender System1 User modeling: previous interactions and user ratings2 Doc modeling: item characteristics.3 Algorithm: recommendations are based on data collected from
previous user interactions (e.g. "these items are similar to whatyou liked before")
Problems and advantages: items need to be described (-),recommendations can be repetitive (-), there is no need to have acritical 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 suggestsonline resources in a health promotion community (Khan SA,University of Columbia, USA)Cancer Sites Recommender: usage of collaborative andcontent-based techniques to recommend prostate cancer webs(Witteman H, University of Toronto, Canada)Suggestion systems for educational resources while navigatingpatient records.MyHealthEducator: an ongoing project where videorecommendations are based on collaborative techniques and aPersonal 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 toautomatic and collaborative techniques.Integration with Personal Health Records (PHR) can improverecommendations and reduce the cold start problem.Collaborative techniques gather aspects such as userpreferences, which are not very common in health educationtailoring.Automatic analysis of User-Generated Content for modelingusers (e.g. Risbot) or modeling documents (e.g. HealthyHarlem)can improve recommendations and increase knowledge aboutthe usersThe knowledge of the human experts about tailoring healtheducation 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. topromote a certain document)Most Recommender Systems are based on popularity and thusmay not lead to good resources (e.g. proanorexia videos arepopular)Integration between web health applications is not yet prominentWeb 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 InformationOverload.Recommender Systems have the potential to facilitate access torelevant educational resources since they are designed for thecontext of Information Overload.Health Education differs a lot from the traditional scenarios ofRecommender 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 ([email protected])