Kelly, Liadh, Suominen, Hanna, Goeuriot, Lorraine, Neves, Mariana, Kanoulas, Evangelos, Li, Dan, Azzopardi, Leif, Spijker, Rene, Zuccon, Guido, Scells, Harrisen and Palotti, Joao (2019) Overview of the CLEF eHealth Evaluation Lab 2019. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science book series (LNCS) (11696). Springer, pp. 322-339. ISBN 9783030285777
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Abstract
In this paper, we provide an overview of the seventh annual
edition of the CLEF eHealth evaluation lab. CLEF eHealth 2019 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 electronic health information in
a multilingual setting. This year’s lab advertised three tasks: Task 1 on
indexing non-technical summaries of German animal experiments with
International Classification of Diseases, Version 10 codes; Task 2 on technology assisted reviews in empirical medicine building on 2017 and 2018
tasks in English; and Task 3 on consumer health search in mono- and
multilingual settings that builds on the 2013–18 Information Retrieval
tasks. In total nine teams took part in these tasks (six in Task 1 and three in Task 2). Herein, we describe the resources created for these tasks and
evaluation methodology adopted. We also 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.
Item Type: | Book Section |
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Additional Information: | Cite as: Kelly L. et al. (2019) Overview of the CLEF eHealth Evaluation Lab 2019. In: Crestani F. et al. (eds) Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science, vol 11696. Springer, Cham. https://doi.org/10.1007/978-3-030-28577-7_26 |
Keywords: | Evaluation; Entity linking; Information retrieval; Health records; High recall; Information extraction; Medical informatics; Self diagnosis; Systematic reviews; Test set generation; Text classification; Text segmentation; |
Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Science and Engineering > Research Institutes > Human Health Institute |
Item ID: | 14368 |
Depositing User: | Liadh Kelly |
Date Deposited: | 22 Apr 2021 14:29 |
Publisher: | Springer |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/14368 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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