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Design and Development of a Multimodal Biomedical Information Retrieval System
Demner-Fushman, Dina,Antani, Sameer,Simpson, Matthew,Thoma, George R. Korean Institute of Information Scientists and Eng 2012 Journal of Computing Science and Engineering Vol.6 No.2
The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients' cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of these resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.
Design and Development of a Multimodal Biomedical Information Retrieval System
Dina Demner-Fushman,Sameer Antani,Matthew Simpson,George R. Thoma 한국정보과학회 2012 Journal of Computing Science and Engineering Vol.6 No.2
The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients’ cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of these resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.
A One-Size-Fits-All Indexing Method Does Not Exist: Automatic Selection Based on Meta-Learning
Jimeno-Yepes, Antonio,Mork, James G.,Demner-Fushman, Dina,Aronson, Alan R. Korean Institute of Information Scientists and Eng 2012 Journal of Computing Science and Engineering Vol.6 No.2
We present a methodology that automatically selects indexing algorithms for each heading in Medical Subject Headings (MeSH), National Library of Medicine's vocabulary for indexing MEDLINE. While manually comparing indexing methods is manageable with a limited number of MeSH headings, a large number of them make automation of this selection desirable. Results show that this process can be automated, based on previously indexed MEDLINE citations. We find that AdaBoostM1 is better suited to index a group of MeSH hedings named Check Tags, and helps improve the micro F-measure from 0.5385 to 0.7157, and the macro F-measure from 0.4123 to 0.5387 (both p < 0.01).
A One-Size-Fits-All Indexing Method Does Not Exist: Automatic Selection Based on Meta-Learning
Antonio Jimeno-Yepes,James G. Mork,Dina Demner-Fushman,Alan R. Aronson 한국정보과학회 2012 Journal of Computing Science and Engineering Vol.6 No.2
We present a methodology that automatically selects indexing algorithms for each heading in Medical Subject Headings (MeSH), National Library of Medicine’s vocabulary for indexing MEDLINE. While manually comparing indexing methods is manageable with a limited number of MeSH headings, a large number of them make automation of this selection desirable. Results show that this process can be automated, based on previously indexed MEDLINE citations. We find that AdaBoostM1 is better suited to index a group of MeSH hedings named Check Tags, and helps improve the micro F-measure from 0.5385 to 0.7157, and the macro F-measure from 0.4123 to 0.5387 (both p < 0.01).