ShARe/CLEF eHealth evaluation lab 2014, task 3: user-centred health information retrieval

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2014. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the effect of using additional information such as a related discharge summary and external resources such as medical ontologies on the IR effectiveness, in a monolingual and in a multilingual context. The participants were allowed to submit up to seven runs for each language, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries

Overview of the CLEF 2018 Consumer Health Search Task

This paper details the collection, systems and evaluation methods used in the CLEF 2018 eHealth Evaluation Lab, Consumer Health Search (CHS) task (Task 3). This task investigates the effectiveness of search engines in providing access to medical information present on the Web for people that have no or little medical knowledge. The task aims to foster advances in the development of search technologies for Consumer Health Search by providing resources and evaluation methods to test and validate search systems. Built upon the the 2013-17 series of CLEF eHealth Information Retrieval tasks, the 2018 task considers both mono- and multilingual retrieval, embracing the Text REtrieval Conference (TREC) -style evaluation process with a shared collection of documents and queries, the contribution of runs from participants and the subsequent formation of relevance assessments and evaluation of the participants submissions. For this year, the CHS task uses a new Web corpus and a new set of queries compared to the previous years. The new corpus consists of Web pages acquired from the CommonCrawl and the new set of queries consists of 50 queries issued by the general public to the Health on the Net (HON) search services. We then manually translated the 50 queries to French, German, and Czech; and obtained English query variations of the 50 original queries. A total of 7 teams from 7 different countries participated in the 2018 CHS task: CUNI (Czech Republic), IMS Unipd (Italy), MIRACL (Tunisia), QUT (Australia), SINAI (Spain), UB-Botswana (Botswana), and UEvora (Portugal)

Adapting SMT Query Translation Reranker to New Languages in Cross-Lingual Information Retrieval

We investigate adaptation of a supervised machine learning model for reranking of query translations to new languages in the context of cross-lingual information retrieval. The model is trained to rerank multiple translations produced by a statistical machine translation system and optimize retrieval quality. The model features do not depend on the source language and thus allow the model to be trained on query translations coming from multiple languages. In this paper, we explore how this affects the final retrieval quality. The experiments are conducted on medical-domain test collection in English and multilingual queries (in Czech, German, French) from the CLEF eHealth Lab series 2013--2015. We adapt our method to allow reranking of query translations for four new languages (Spanish, Hungarian, Polish, Swedish). The baseline approach, where a single model is trained for each source language on query translations from that language, is compared with a model co-trained on translations from the three original languages

An analysis of query difficulty for information retrieval in the medical domain

We present a post-hoc analysis of a benchmarking activity for information retrieval (IR) in the medical domain to determine if performance for queries with different levels of complexity can be associated with different IR methods or techniques. Our analysis is based on data and runs for Task 3 of the CLEF 2013 eHealth lab, which provided patient queries and a large medical document collection for patient centred medical information retrieval technique development. We categorise the queries based on their complexity, which is defined as the number of medical concepts they contain. We then show how query complexity affects performance of runs submitted to the lab, and provide suggestions for improving retrieval quality for this complex retrieval task and similar IR evaluation tasks

CLEF eHealth 2019 Evaluation Lab

Since 2012 CLEF eHealth has focused on 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 offers three tasks: Task 1 on multilingual information extraction; Task 2 on technology assisted reviews in empirical medicine; and Task 3 on consumer health search in mono- and multilingual settings. Herein, we describe the CLEF eHealth evaluation series to-date and then present the 2019 tasks, evaluation methodology, and resources

Task 2: ShARe/CLEF eHealth evaluation lab 2014

This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template lling of disorder attributes. The task was comprised of two subtasks: attribute normalization (task 2a) and cue identication (task 2b).We instructed participants to develop a system which either kept or updated a default attribute value for each task. Participant systems were evaluated against a blind reference standard of 133 discharge summaries using Accuracy (task 2a) and F-score (task 2b). In total, ten teams participated in task 2a, and three teams in task 2b. For task 2a and 2b, the HITACHI team systems (run 2) had the highest performances, with an overall average average accuracy of 0.868 and F1-score (strict) of 0.676, respectively

An enhanced concept based approach medical information retrieval to address readability, vocabulary and presentation issues

Querying of health information retrieval for health advice has now become a general and notable task performed by individuals on the Internet. However, the failure of the existing approaches to integrate program modules that would address the information needs of all categories of end-users remains. This study focused on proposing an improved framework and designing an enhanced concept based approach (ECBA) for medical information retrieval that would better address readability, vocabulary mismatched and presentation issues by generating medical discharge documents and medical search queries results in both medical expert and layman’s forms. Three special program modules were designed and integrated in the enhanced concept based approach namely: medical terms control module, vocabulary controlled module and readability module to specifically address the information needs of both medical experts and laymen end-users. Eight benched marked datasets namely: Medline, UMLS, MeSH, Metamap, Metathesaurus, Diagnosia 7, Khresmoi Project 6 and Genetic Home Reference were used in validating the systems performance. Additionally, the ECBA was compared using three existing approaches such as concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI). The evaluation was conducted using the performance and statistical metrics: P@40, NDCG@40, MAP, Analysis of Variance (ANOVA) and Turkey HSD Tests. The outcome of the final experimental results obtained shows that, the ECBA consistently obtained above 93% accuracy rate results on Medline, UMLS and MeSH Datasets, 92% on Metamap, Metathesaurus and Diagnosia 7 datasets and 91% on Khresmoi Project 6 and Genetic Home Reference datasets. Also, the statistical analysis performance results obtained by each of the four approaches: ECBA, CBA, QLM and LSI shows that, there is a significant difference among their Mean Scores, hence, the null hypothesis of no significant difference was rejected

Test collections for medical information retrieval evaluation

The web has rapidly become one of the main resources for medical information for many people: patients, clinicians, medical doctors, etc. Measuring the effectiveness with which information can be retrieved from web resources for these users is crucial: it brings better information to professionals for better diagnosis, treatment, patient care; and helps patients and relatives get informed on their condition. Several existing information retrieval (IR) evaluation campaigns have been developed to assess and improve medical IR methods, for example the TREC Medical Record Track [11] and TREC Genomics Track [10]. These campaigns only target certain type of users, mainly clinicians and some medical professionals: queries are mainly centered on cohorts of records describing a specific patient cases or on biomedical reports. Evaluating search effectiveness over the many heterogeneous online medical information sources now available, which are increasingly used by a diverse range of medical professionals and, very importantly, the general public, is vital to the understanding and development of medical IR. We describe the development of two benchmarks for medical IR evaluation from the Khresmoi project. The first of these has been developed using existing medical query logs for internal research within the Khresmoi project and targets both medical professionals and general public; the second has been created in the framework of a new CLEFeHealth evaluation campaign and is designed to evaluate patient search in context