Adaptive Information for Consumers of Healthcare

Cawsey, Alison; Grasso, Floriana; Paris, Cécile

doi:10.1007/978-3-540-72079-9_15

Cited by 33 publications

(23 citation statements)

References 36 publications

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“…The acquired knowledge is then used to build a user model, which serves as input to system design. As healthcare users' understanding and interpretation of medical terminology may be quite different to that of healthcare professionals (Alpay, Toussaint, & Zwetsloot-Schonk, 2004;Bromme, Jucks, & Wagner, 2005;Cawsey, Grasso, & Paris, 2007), knowledge acquisition that has been conducted with the intended users rather than with medical experts can lead to systems that are more useful and comprehensible for users (Ashraf et al, 2002;Corry, Gjerlufsen, & Olsen, 2005). Based on Knowledge Acquisition with parents of NNU babies, our research constructs a user model that informs the adaptation of messages across the social network.…”

Section: Adaptive Ehealthmentioning

confidence: 99%

Providing Adaptive Health Updates Across the Personal Social Network

Moncur

Masthoff

Reiter

et al. 2014

Human–Computer Interaction

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This article presents research conducted to establish how information is shared across the personal social network in the sensitive context of a health crisis. We worked with parents of very sick babies who were cared for in a hospital's Neonatal Unit (NNU). Through a combination of interviews, a focus group, and surveys, we developed a user model of the information that parents wanted to share, and how they adapted this information to individual recipients. We then developed a prototype software tool which created adaptive updates for members of the parents' social network. The updates contained summaries of large volumes of complex medical data about the baby, nonmedical information about the parents, and practical information about the hospital. Updates were automatically adapted to individual members of parents' social networks, based on our user model. The tool was evaluated in a large NNU in the United Kingdom with parents of babies who were currently being cared for in the unit.

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Section: Adaptive Ehealthmentioning

confidence: 99%

Providing Adaptive Health Updates Across the Personal Social Network

Moncur

Masthoff

Reiter

et al. 2014

Human–Computer Interaction

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“…These are people who seek information on various aspects related to health and well being, like health promotion, disease prevention, management of long term conditions, and so on. But as the amount of information available on the Web continues to grow, it is recognised that there is a need to design and develop intelligent systems capable of tailoring information for health care consumers (Hawkins et al 2008), whereby only providing appropriate, timely and relevant information and exploiting this great potential to enhance health information and education through web (or mobile) delivery (Cawsey et al 2007). …”

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confidence: 99%

Preface to the special issue on personalization for e-health

Grasso

Paris

2011

User Model User-Adap Inter

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“…By reading this volume you will gain a detailed understanding of how personalisation techniques have been explored in a wide variety of task contexts, from searching and navigating the Web (see Chapter 6 [61] in this book) to e-commerce (see Chapter 16 [38] in this book), healthcare (see Chapter 15 [23]) and e-learning (Chapter 1 [18] in this book) applications.…”

Section: Getting Personalmentioning

confidence: 99%

Case-Based Recommendation

Smyth

Lecture Notes in Computer Science

154

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Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,

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Adaptive Information for Consumers of Healthcare

Cited by 33 publications

References 36 publications

Providing Adaptive Health Updates Across the Personal Social Network

Providing Adaptive Health Updates Across the Personal Social Network

Preface to the special issue on personalization for e-health

Case-Based Recommendation

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