Enriching queries with user preferences in healthcare Tesfa Tegegne ⇑ , Th.P. (Theo) van der Weide Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands article info Article history: Received 12 September 2012 Received in revised form 7 March 2014 Accepted 8 March 2014 Keywords: Query enrichment User preference Conditional preference abstract Query enrichment is a process of dynamically enhancing a user query based on her prefer- ences and context in order to provide a personalized answer. The central idea is that differ- ent users may find different services relevant due to different preferences and contexts. In this paper, we present a preference model that combines user preferences, user context, domain knowledge to enrich the initial user query. We use CP-nets to rank the preferences using implicit and explicit user preferences and domain knowledge. We present some algo- rithms for preferential matching. We have implemented the proposed model as a proto- type. The initial results look promising. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction Technological advances have brought tremendous progress in healthcare. Examples are: electronic medical records, mobile health and eHealth, videoconferencing, medical decision making, remote monitoring and many more. Besides these, healthcare now can provide personalized health decisions for patients and empower patients to participate in the decision making. Recently, patient empowerment and engagement has gotten a big boost. Patient-centeredness helps patients and families and/or doctors to make informed healthcare decisions during diagnosis and treatment (Arnold, 2013). However, these benefits of technology in healthcare are not equitably available for developing countries with their restricted infrastructure and limited resources. Illiteracy, little medical knowledge and poor or ambiguous healthcare queries are factors that hinder the realization of patient empowerment. Low infrastructure, unavailability of technologies, costs and shortage and high turnover of clinicians are some of the obstacles to realize patient-centeredness in particular and eHealth in general. These challenges and opportunities have triggered the work in this paper. Especially we have focused on the Ethi- opian situation (see (Berhan, 2008; Serra, Serneels, Lindelow, & Montalvo, 2010 in general and Tegegne & Weide, 2011; Tegegne, Kanagwa, & Weide, 2010) for a more detailed description). In this paper we focus on a basic architecture to support health workers. This architecture supports the health worker to make a diagnosis, and then tries to find the best treatment for the patient at hand adapted to the environment of this patient. The dialogue support principles have been discussed in Tegegne and Weide (2013) and are outside the scope of this paper. In this paper we focus on the enrichment of the initial (medical) query, before matching it against a body of medical knowledge and making a prioritization of the possible treatments. Rather than setting up an advanced expert system to cover for any medical case, we use a basic Information Retrieval approach to match a diagnosis with patterns defined for typical diseases. Then we use personal and environmental informa- tion to rank the associated treatments. The diagnosis can be seen as the initial query, that is enriched by personal and environmental information. http://dx.doi.org/10.1016/j.ipm.2014.03.004 0306-4573/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +31 685177546. E-mail addresses: t.tegegne@cs.ru.nl (T. Tegegne), tvdw@cs.ru.nl (Th.P. (Theo) van der Weide). Information Processing and Management 50 (2014) 599–620 Contents lists available at ScienceDirect Information Processing and Management journal homepage: www.elsevier.com/locate/infoproman