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

BMC Medical Informatics and Decision Making

Database

Miguel García-Remesal¹  Guillermo de la Calle¹  Nelida Nkumu-Mbomio¹  Victor Maojo¹  Casimir Kulikowski² 

[1] Biomedical Informatics Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, 28660, Boadilla del MonteMadrid, Spain;Department of Computer Science, Rutgers – The State University of New Jersey, 08855, New jersey, USA;

关键词: Medical informatics;    Cataloging;    Classification;    Software resources;    Information storage and retrieval;    Search engine;    Database;    Information management;    Folksonomies;    Social tagging;   

DOI  :  10.1186/1472-6947-12-82

 received in 2012-02-17, accepted in 2012-07-24,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundOver the past 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 the Medical Informatics (MI) field, so that locating and accessing them currently remains a difficult and time-consuming task.DescriptionWe have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. We define informatics resources as all those elements that constitute, serve to define or are used by informatics systems, ranging from architectures or development methodologies to terminologies, vocabularies, databases or tools. 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 classification schemas 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 classification schemas. The classification algorithm identifies the categories associated with resources and annotates them accordingly. The database is then populated with this data after manual curation and validation.ConclusionsWe 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 contains 609 resources at the time of writing and is available at http://www.gib.fi.upm.es/eMIR2. 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.

【 授权许可】

CC BY   
© de la Calle et al.; licensee BioMed Central Ltd. 2012