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01.12.2012 | Database | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

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

BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Guillermo de la Calle, Miguel García-Remesal, Nelida Nkumu-Mbomio, Casimir Kulikowski, Victor Maojo
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6947-12-82) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

GDLC conceived and participated in the design of the study and drafted the manuscript. NNM participated in the design of the study and implemented the system. MGR participated in the design of the study and drafted the manuscript. CK helped to draft and edit the manuscript. VM conceived the study and helped to draft the manuscript. All authors read and approved the final manuscript.



Over 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.


We 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.


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 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.
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