e-MIR2: a public online inventory of medical informatics resources

Maojo Garcia, Victor Manuel; Calle, Guillermo de la; García Remesal, Miguel; Kulikowski, Casimir y Nkumu-Mbomio, Nelida (2012). e-MIR2: a public online inventory of medical informatics resources. "Bmc Medical Informatics And Decision Making", v. 12 (n. null); pp. 82-83. ISSN 1472-6947. https://doi.org/10.1186/1472-6947-12-82.

Descripción

Título: e-MIR2: a public online inventory of medical informatics resources
Autor/es:
  • Maojo Garcia, Victor Manuel
  • Calle, Guillermo de la
  • García Remesal, Miguel
  • Kulikowski, Casimir
  • Nkumu-Mbomio, Nelida
Tipo de Documento: Artículo
Título de Revista/Publicación: Bmc Medical Informatics And Decision Making
Fecha: Agosto 2012
Volumen: 12
Materias:
Palabras Clave Informales: Medical informatics, Cataloging, Classification, Software resources, Information storage and retrieval, Search engine, Database, Information management, Folksonomies, Social tagging, Informática médica, Catalogación, Clasificación, Recursos de software, Almacenamiento y recuperación de información, Motor de búsqueda, Bases de datos, Getión de información, Folksonomías, Etiquetado social.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Background. Over the last years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for Medical Informatics (MI) field, so that locating and accessing them currently remains a hard and time-consuming task. Description. We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources? names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different taxonomies by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the taxonomies. The classification algorithm identifies the categories associated to resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions. We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contained 282 resources at the time of writing. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.

Más información

ID de Registro: 16874
Identificador DC: http://oa.upm.es/16874/
Identificador OAI: oai:oa.upm.es:16874
Identificador DOI: 10.1186/1472-6947-12-82
URL Oficial: http://www.biomedcentral.com/1472-6947/12/82
Depositado por: Memoria Investigacion
Depositado el: 09 Sep 2013 16:01
Ultima Modificación: 21 Abr 2016 17:13
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