|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Author(s):||Carmichael, Alexander F B|
Gunn, George J
|Title:||Ir-Man: An Information Retrieval Framework for Marine Animal Necropsy Analysis|
|Citation:||Carmichael AFB, Bhowmik D, Baily J, Brownlow A, Gunn GJ & Reeves A (2020) Ir-Man: An Information Retrieval Framework for Marine Animal Necropsy Analysis. In: 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2020), Virtual, 21.09.2020-24.09.2020. New York: ACM. https://acm-bcb.org/; https://doi.org/10.1145/3388440.3412417|
|Conference Name:||11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2020)|
|Conference Dates:||2020-09-21 - 2020-09-24|
|Abstract:||This paper proposes Ir-Man (Information Retrieval for Marine Animal Necropsies), a framework for retrieving discrete information from marine mammal post-mortem reports for statistical analysis. When a marine mammal is reported dead after stranding in Scotland, the carcass is examined by the Scottish Marine Animal Strandings Scheme (SMASS) to establish the circumstances of the animal's death. This involves the creation of a 'post-mortem' (or necropsy) report , which systematically describes the body. These semi-structured reports record lesions (damage or abnormalities to anatomical regions) as well as other observations. Observations embedded within these texts are used to determine cause of death. While a cause of death is recorded separately, many other descriptions may be of pathological and epidemiological significance when aggregated and analysed collectively. As manual extraction of these descriptions is costly, time consuming and at times erroneous, there is a need for an automated information retrieval mechanism which is a non-trivial task given the wide variety of possible descriptions, pathologies and species. The Ir-Man framework consists of a new ontology, a lexicon of observations and anatomical terms and an entity relation engine for information retrieval and statistics generation from a pool of necropsy reports. We demonstrate the effectiveness of our framework by creating a rule-based binary classifier for identifying bottlenose dolphin attacks (BDA) in harbour porpoise gross pathology reports and achieved an accuracy of 83.4%.|
|Status:||AM - Accepted Manuscript|
|Rights:||© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the ACM Digital Library as part of ACM-BCB 2020 proceedings|
|acm_bcb_2020.pdf||Fulltext - Accepted Version||858.22 kB||Adobe PDF||View/Open|
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