João R. M. Palotti

How users search and what they search for in the medical domain

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.

Ranking Health Web Pages with Relevance and Understandability

We propose a method that integrates relevance and understandability to rank health web documents. We use a learning to rank approach with standard retrieval features to determine topical relevance and additional features based on readability measures and medical lexical aspects to determine understandability. Our experiments measured the effectiveness of the learning to rank approach integrating understandability on a consumer health benchmark. The findings suggest that this approach promotes documents that are at the same time topically relevant and understandable.

Diagnose This If You Can

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

The Influence of Pre-processing on the Estimation of Readability of Web Documents

This paper investigates the effect that text pre-processing approaches have on the estimation of the readability of web pages. Readability has been highlighted as an important aspect of web search result personalisation in previous work. The most widely used text readability measures rely on surface level characteristics of text, such as the length of words and sentences. We demonstrate that different tools for extracting text from web pages lead to very different estimations of readability. This has an important implication for search engines because search result personalisation strategies that consider users reading ability may fail if incorrect text readability estimations are computed.

Assessors Agreement: A Case Study Across Assessor Type, Payment Levels, Query Variations and Relevance Dimensions

Relevance assessments are the cornerstone of Information Retrieval evaluation. Yet, there is only limited understanding of how assessment disagreement influences the reliability of the evaluation in terms of systems rankings. In this paper we examine the role of assessor type (expert vs. layperson), payment levels (paid vs. unpaid), query variations and relevance dimensions (topicality and understandability) and their influence on system evaluation in the presence of disagreements across assessments obtained in the different settings. The analysis is carried out in the context of the CLEF 2015 eHealth Task 2 collection and shows that disagreements between assessors belonging to the same group have little impact on evaluation. It also shows, however, that assessment disagreement found across settings has major impact on evaluation when topical relevance is considered, while it has no impact when understandability assessments are considered.

Fixed-Cost Pooling Strategies Based on IR Evaluation Measures

Recent studies have reconsidered the way we operationalise the pooling method, by considering the practical limitations often encountered by test collection builders. The biggest constraint is often the budget available for relevance assessments and the question is how best – in terms of the lowest pool bias – to select the documents to be assessed given a fixed budget. Here, we explore a series of 3 new pooling strategies introduced in this paper against 3 existing ones and a baseline. We show that there are significant differences depending on the evaluation measure ultimately used to assess the runs. We conclude that adaptive strategies are always best, but in their absence, for top-heavy evaluation measures we can continue to use the baseline, while for P@100 we should use any of the other non-adaptive strategies.

Fixed budget pooling strategies based on fusion methods

The empirical nature of Information Retrieval (IR) mandates strong experimental practices. The Cranfield/TREC evaluation paradigm represents a keystone of such experimental practices. Within this paradigm, the generation of relevance judgments has been the subject of intense scientific investigation. This is because, on one hand, consistent, precise and numerous judgements are key to reduce evaluation uncertainty and test collection bias; on the other hand, however, relevance judgements are costly to collect. The selection of which documents to judge for relevance (known as pooling) has therefore great impact in IR evaluation. In this paper, we contribute a set of 8 novel pooling strategies based on retrieval fusion methods. We show that the choice of the pooling strategy has significant effects on the cost needed to obtain an unbiased test collection; we also identify the best performing pooling strategy according to three evaluation measure.

CLEF 2017 eHealth evaluation lab overview

In this paper we provide an overview of the fifth edition of the CLEF eHealth evaluation lab. CLEF eHealth 2017 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring eHealth information in a multilingual setting. This year’s lab offered three tasks: Task 1 on multilingual information extraction to extend from last year’s task on French corpora, Task 2 on technologically assisted reviews in empirical medicine as a new pilot task, and Task 3 on patient-centered information retrieval (IR) building on the 2013-16 IR tasks. In total 32 teams took part in these tasks (11 in Task 1, 14 in Task 2, and 7 in Task 3). We also continued the replication track from 2016. Herein, we describe the resources created for these tasks, evaluation methodology adopted and provide a brief summary of participants of this year’s challenges and results obtained. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research and development.

User intent behind medical queries: an evaluation of entity mapping approaches with metamap and freebase

This work focuses on understanding the user intent in the medical domain. The combination of Semantic Web and information retrieval technologies promises a better comprehension of user intents. Mapping queries to entities using Freebase is not novel, but so far only one entity per query could be identified. We overcome this limitation using annotations provided by Metamap. Also, different approaches to map queries to Freebase are explored and evaluated. We propose an indirect evaluation of the mappings, through user intent defined by classes such as Symptoms, Diseases or Treatments. Our experiments show that by using the concepts annotated by Metamap it is possible to improve the accuracy and F1 performances of mappings from queries to Freebase entities.

Query Variations and their Effect on Comparing Information Retrieval Systems

We explore the implications of using query variations for evaluating information retrieval systems and how these variations should be exploited to compare system effectiveness. Current evaluation approaches consider the availability of a set of topics (information needs), and only one expression of each topic in the form of a query is used for evaluation and system comparison. While there is strong evidence that considering query variations better models the usage of retrieval systems and accounts for the important user aspect of user variability, it is unclear how to best exploit query variations for evaluating and comparing information retrieval systems. We propose a framework for evaluating retrieval systems that explicitly takes into account query variations. The framework considers both the system mean effectiveness and its variance over query variations and topics, as opposed to current approaches that only consider the mean across topics or perform a topic-focused analysis of variance across systems. Furthermore, the framework extends current evaluation practice by encoding: (1) user tolerance to effectiveness variations, (2) the popularity of different query variations, and (3) the relative importance of individual topics. These extensions and our findings make information retrieval comparisons more aligned with user behaviour.