Consumer Health Search at CLEF eHealth 2021

This paper details materials, methods, results, and analyses of the Consumer Health Search Task of the CLEF eHealth 2021 Evaluation Lab. This task investigates the effectiveness of information retrieval (IR) approaches in providing access to medical information to laypeople. For this a TREC-style evaluation methodology was applied: a shared collection of documents and queries is distributed, participants’ runs received, relevance assessments generated, and participants’ submissions evaluated. The task generated a new representative web corpus including web pages acquired from a 2021 CommonCrawl and social media content from Twitter and Reddit, along with a new collection of 55 manually generated layperson medical queries and their respective credibility, understandability, and topicality assessments for returned documents. This year’s task focused on three subtask: (i) ad-hoc IR, (ii) weakly supervised IR, and (iii) document credibility prediction. In total, 15 runs were submitted to the three subtasks: eight addressed the ad-hoc IR task, three the weakly supervised IR challenge, and 4 the document credibility prediction challenge. As in previous years, the organizers have made data and tools associated with the task available for future research and development

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Background Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. Methods Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. Results Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. Conclusions ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.</p

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

The Scholarly Impact and Strategic Intent of CLEF eHealth Labs from 2012 to 2017

International audienc

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

AIUCD2019 - Book of Abstracts

L’ottava edizione del Convegno nazionale dell’Associazione per l’Informatica Umanistica e la Cultura Digitale, collocandosi nel solco delle precedenti edizioni, si pone l’obiettivo di costituire un punto di incontro tra studiosi delle diverse discipline che - a vario titolo - fanno capo alle cosiddette Digital Humanities: umanisti digitali, informatici, linguisti, storici, archeologi, musicologi, filologici e non solo. Il tema principale di questa ottava edizione è la “Didattica e ricerca al tempo delle Digital Humanities”. Si tratta di un ambito vasto che accomuna pratiche e settori di studio interdisciplinari e multidisciplinari e che riguarda i mutamenti della didattica e della ricerca nell'era digitale

AIUCD2019 - Book of Abstracts

L’ottava edizione del Convegno nazionale dell’Associazione per l’Informatica Umanistica e la Cultura Digitale, collocandosi nel solco delle precedenti edizioni, si pone l’obiettivo di costituire un punto di incontro tra studiosi delle diverse discipline che - a vario titolo - fanno capo alle cosiddette Digital Humanities: umanisti digitali, informatici, linguisti, storici, archeologi, musicologi, filologici e non solo. Il tema principale di questa ottava edizione è la “Didattica e ricerca al tempo delle Digital Humanities”. Si tratta di un ambito vasto che accomuna pratiche e settori di studio interdisciplinari e multidisciplinari e che riguarda i mutamenti della didattica e della ricerca nell'era digitale