Abstract
In this paper we describe the participation of InfoLab in the patient-centred information retrieval task of the CLEF eHealth 2016 lab. We analyse the performance of several query expansion strategies using difierent sources of terms and difierent methods to select the terms to be added to the original query. One of the strategies uses pseudo relevance feedback for term selection. The other strategies use external sources such as Wikipedia articles and definitions from the UMLS Metathesaurus for term selection. In the end, readability metrics such as SMOG, FOG and Flesch-Kincaid were used to re-rank the documents retrieved using the expanded queries. As the relevance and readability assessments are not available we can't make any conclusion regarding the results of our approaches.

Abstract
Search engines typically estimate relevance using features of the documents. We believe that several features from the user and task can also contribute to this process. In the health domain there are specific characteristics of web documents that can also add value to this estimation. In the present work, using a dataset composed by set of annotated web pages and their assessment by a set of users regarding their relevance and comprehension, we analyse what characteristics affect documents' relevance and what characteristics influence how well users comprehend them. We have conducted a bivariate analysis using characteristics of the above data collection. The strongest relations we have found are linked to the task features, suggesting a direct association between tasks' clarity and easiness and both the relevance and the comprehension of the content. The language of the document, its medical certification, the update status, the content in pathology definitions, the content in prevention, prognosis and treatment information, are other characteristics valued by consumers in terms of relevance. Users' previous experience on health searches and, particularly, on the topic being searched, their gender, the language and terminology of their queries were shown to be related to their success in the search tasks. We have also found that lay terminology, knowledge about the medico-scientific terms and the language of the documents are good indicators of comprehension. Documents containing links and testimonies, and the ones recently updated were observed to be better understood by users, as well as blog posts and comments. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
In this paper we propose a multilingual method to identify health-related queries and classify them into health categories. Our method uses a consumer health vocabulary and the Unified Medical Language System semantic structure to compute the association degree of a query to medical concepts and categories. This method can be applied in different languages with translated versions of the health vocabulary. To evaluate its efficacy and applicability in two languages we used two manually classified sets of queries, each on a different language. Results are better for the English sample where a distance of 0.38 to the ROC optimal point (0,1) was obtained. This shows some influence of the translation in the method's performance.

Abstract
We conducted a user study to analyze how health literacy, topic familiarity and the terminology used in past queries affect query behavior in health searches. We found that users with inadequate health literacy have less success in web searches and show more difficulties in query formulation. These users and the ones not familiar with the topic use medico-scientific terminology less often than users with more health literacy and topic familiarity. We conclude that search engines should help these groups of users in query formulation and, since technical documents stimulate the use of medico-scientific terminology in query reformulation, mechanisms like query suggestion can have long-term benefits. © 2013 Springer-Verlag.

Abstract
We present a case study of quality evaluation of online health information. Two participants were selected from a health information search (HIS) study, in which we are investigating consumers' evaluation of the quality of online health information. The selected cases offered a rare example of two almost exactly opposite eye-movement patterns on the same webpage. To better understand the differences in these patterns, we investigated participants' cognitive evaluation processes by examining their textual explanations collected in post-task questionnaires and verbal explanations collected in the retrospective think-aloud (RTA) sessions. We discuss how eHealth literacy and personality scores may be related to the behavioral differences. The findings of this case study inform the formulation of hypotheses for full data analysis of the HIS study, as well as future research addressing behavior patterns and factors affecting consumers' quality evaluation of online health information. Copyright © 2017 by Association for Information Science and Technology

Abstract
The health domain is rich in specific vocabulary and information structures. Previous work on this area includes the collection of this information in information systems. However, the language of these can limit their use. To overcome this, we present Health Translations, a web application that uses crowd-sourcing to translate a large vocabulary set that, currently, is only available in English. To increase usage, gamification methods are applied that reward both the quantity of collaboration and the quality of it. When completed, these translations can be made available without costs to the research community. This paper presents the platform as a responsive web application.