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Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Suominen, Hanna and Salanterä, Sanna and Velupillai, Sumithra and Chapman, Wendy and Savova, Guergana and Elhadad, Noemie and Pradhan, Sameer and South, Brett R. and Mowery, Danielle L. and Jones, Gareth J.F. and Leveling, Johannes and Kelly, Liadh and Goeuriot, Lorraine and Martinez, David and Zuccon, Guido (2013) Overview of the ShARe/CLEF eHealth Evaluation Lab 2013. In: 4th International Conference of the CLEF Initiative (CLEF 2013), 23-26 Sept 2013, Valencia, Spain. ISBN 978-3-642-40802-1

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Abstract

Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) in clinical reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients’ information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identified clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Medical Information
Subjects:Computer Science > Computational linguistics
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in:Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Lecture Notes in Computer Science 8138. Springer Berlin Heidelberg. ISBN 978-3-642-40802-1
Publisher:Springer Berlin Heidelberg
Official URL:http://www.springer.com/computer/ai/book/978-3-642-40801-4
Copyright Information:© 2013 Springer The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20082
Deposited On:10 Sep 2014 13:24 by Lorraine Goeuriot. Last Modified 01 Feb 2017 13:05

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