IPHealthplataforma inteligente basada en open, linked y big data para la toma de decisiones y aprendizaje en el ámbito de la salud

  1. Manuel de Buenaga
  2. Diego Gachet
  3. Manuel J. Maña
  4. Jacinto Mata
  5. L. Borrajo
  6. E. L. Lorenzo
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Ano de publicación: 2015

Número: 55

Páxinas: 161-164

Tipo: Artigo

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Outras publicacións en: Procesamiento del lenguaje natural

Resumo

The IPHealth project's main objective is to design and implement a platform with services that enable an integrated and intelligent access to related in the biomedical domain. We propose three usage scenarios: (i) assistance to healthcare professionals during the decision making process at clinical settings, (ii) access to relevant information about their health status and dependent chronic patients and (iii) to support evidence-based training of new medical students. Most effective techniques are proposed for reveral NLP tecniques and extraction of information from large data sets from sets of sensors and using open data. A Web application framework and an architecture that would enable integration of processes and techniques of text and data mining will be designed. Also, this architecture have to allow an integration of information in a fast, consistent and reusable (via plugins) way.

Referencias bibliográficas

  • Borrajo, L., Seara, A., Iglesias, E.L. 2015 TCBR-HMM: An HMM-ba se d text classifier with a CBR system, Applied Soft Computing, Volume 26, January2015 , Pages 463-473
  • Crespo, M., Mata, J., Maña, M.J. 2013. Improving image retrieval effectiveness via query expansion using MeSH hierarchical structure Journal of the American Medical Informatics Association, 20(6): 1014-1020
  • Cruz, N.P., Maña, M.J., Mata, J., Pachón, V. 2012. A Machine Learning Approach to Negation and Speculation Detection in Clinical Texts, Journal of the American Society for Information Science and Technology, 63(7): 1398- 1410.
  • Demner-Fushman D, Mork J, Shooshan S, Aronson A. 2010. UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text. Journal of Biomedical Informatics, 43 (4):587-594.
  • Fernald, G.H., Capriotti, E., Daneshjou, R., Karczewski, K.J., Altman, R.B. 2011. Bioinformatics Challenges for Personalized Medicine. Bioinformatics, 27(13):1741-8
  • Gachet Páez, D., Aparicio, F., de Buenaga, M., y Ascanio, J. R. 2014. Big data and IoT for chronic patients monitoring, UCAmI 2014, pp. 416-423, Belfast, UK
  • Kumar, D. 2011. The personalised medicine. A paradigm of evidence-based medicine. Ann Ist Super Sanita., 47(1):31-40.
  • Pandeya S, Voorsluysa W, Niua S, et al. 2012 An autonomic cloud environment for hosting ECG data analysis services. Future Gener Comp Syst 2012;28:147–54
  • Prajapati, V. 2013. Big data analytics with R and Hadoop. Packt Publishing Ltd.
  • Romero, R., Seara, A., Iglesias, E.L. and Borrajo, L. 2014. BioClass: A Tool for Biomedical Text Classification. En Proceedings of the 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014), pp. 243-251, Salamanca.
  • Sahoo SS, Jayapandian C, Garg G, Kaffashi F, Chung S, Bozorgi A, et al. 2014 Heart beats in the cloud: distributed analysis of electrophysiological big data using cloud computing for epilepsy clinical research. J Am Med Inform Assoc 2014. Mar-Apr;21(2):263-71.
  • Villa, M., Aparicio, F., Maña, M.J. Buenaga, M. 2012. A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition. En Proceedings of the International Conference on Intelligent User Interfaces (IUI), pp. 61-70, Lisboa.
Fonte de datos: Dialnet