Authors:
Ana Jimenez-Castellanos
1
;
Izaskun Fernández
2
;
David Perez-Rey
1
;
Elisa Viejo
3
;
Francisco Javier Díez
2
;
Xabier García de Kortazar
2
;
Miguel García-Remesal
1
;
Víctor Maojo
1
;
Antonio Cobo
4
and
Francisco del Pozo
5
Affiliations:
1
Universidad Politécnica de Madrid, Spain
;
2
Tekniker-IK4, Spain
;
3
Technical University of Madrid (CTB – UPM), Spain
;
4
Technical University of Madrid (CTB – UPM); 4Biomedical Research Networking Center in Bioengineering and Biomaterials and Nanomedicine (CIBER –BBN), Spain
;
5
Technical University of Madrid (CTB – UPM); Biomedical Research Networking Center in Bioengineering and Biomaterials and Nanomedicine (CIBER –BBN), Spain
Keyword(s):
Electronic Health Record, Search engines, Literature retrieval, Integration, Federated search.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cloud Computing
;
Collaboration and e-Services
;
Complex Systems Modeling and Simulation
;
Data Engineering
;
Data Mining
;
Databases and Datawarehousing
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Design and Development Methodologies for Healthcare IT
;
e-Business
;
e-Health
;
e-Health for Public Health
;
Enterprise Information Systems
;
Health Information Systems
;
Integration/Interoperability
;
Interoperability
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Platforms and Applications
;
Semantic Interoperability
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
;
Software Agents and Internet Computing
;
Software and Architectures
;
Symbolic Systems
Abstract:
Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians