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The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping
This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710756/ https://www.ncbi.nlm.nih.gov/pubmed/26793094 http://dx.doi.org/10.3389/fninf.2015.00030 |
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author | Ashish, Naveen Dewan, Peehoo Toga, Arthur W. |
author_facet | Ashish, Naveen Dewan, Peehoo Toga, Arthur W. |
author_sort | Ashish, Naveen |
collection | PubMed |
description | This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal. |
format | Online Article Text |
id | pubmed-4710756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47107562016-01-20 The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping Ashish, Naveen Dewan, Peehoo Toga, Arthur W. Front Neuroinform Neuroscience This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal. Frontiers Media S.A. 2016-01-13 /pmc/articles/PMC4710756/ /pubmed/26793094 http://dx.doi.org/10.3389/fninf.2015.00030 Text en Copyright © 2016 Ashish, Dewan and Toga. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ashish, Naveen Dewan, Peehoo Toga, Arthur W. The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title | The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title_full | The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title_fullStr | The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title_full_unstemmed | The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title_short | The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping |
title_sort | gaain entity mapper: an active-learning system for medical data mapping |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710756/ https://www.ncbi.nlm.nih.gov/pubmed/26793094 http://dx.doi.org/10.3389/fninf.2015.00030 |
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