Skip to main content

Ontologies for Early Detection of the Alzheimer Disease and Other Neurodegenerative Diseases

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11486))

Abstract

Nowadays technologies allow an exponential generation of biomedical data, which must be indexed according to some standard criteria to be useful to the scientific and medical community, being neurology one of the areas in which the standardization is more necessary. Ontologies have been highlighted as one of the best options, with their capability of homogenise information, allowing their integration with other kind of information, and the inference of new information based on the data that is stored. We analyse and compare the approaches taken by different research groups inside the area of the Alzheimer’s disease, and the ontologies they developed with the objective of providing a common framework to standardize information, data recovery or as a part of an expert system. However, to make this approach work the ontologies must be maintained over the time, a critical point which is not been followed by any of the ontologies reviewed.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Arp, R., Smith, B.: Realizable entities in basic formal ontology. In: Proceedings of Bio-Ontologies Workshop, Intelligent Systemas for Molecular Biology (ISMB), p. 10 (2011)

    Google Scholar 

  2. Batrancourt, B., Dojat, M., Gibaud, B., Kassel, G.: A multilayer ontology of instruments for neurological behavioral and cognitive assessments. Neuroinformatics 13(1), 93–110 (2015)

    Article  Google Scholar 

  3. Blake, J.A., Bult, C.J.: Beyond the data deluge: data integration and bio-ontologies. J. Biomed. Inf. 39(3), 314–320 (2006)

    Article  Google Scholar 

  4. Burgun, A., Bodenreider, O.: Accessing and integrating data and knowledge for biomedical research. Yearb. Med. Inf. 17(01), 91–101 (2008)

    Article  Google Scholar 

  5. Ciccarese, P., et al.: The SWAN biomedical discourse ontology. J. Biomed. Inf. 41(5), 739–751 (2008)

    Article  Google Scholar 

  6. Costa, F.F.: Big data in biomedicine. Drug Disc. Today 19(4), 433–440 (2014)

    Article  Google Scholar 

  7. Cox, A.P., Jensen, M., Ruttenberg, A., Szigeti, K., Diehl, A.D.: Measuring cognitive functions: hurdles in the development of the neuropsychological testing ontology. In: Proceedings of the 4th International Conference on Biomedical Ontology 2013, Montreal, Canada, p. 6, July 2013

    Google Scholar 

  8. Decety, J., Cacioppo, J.: Frontiers in human neuroscience: the golden triangle and beyond. Perspect. Psychol. Sci. 5(6), 767–771 (2010)

    Article  Google Scholar 

  9. Gao, Y., et al.: SWAN: a distributed knowledge infrastructure for Alzheimer disease research. Web Seman.: Sci. Serv. Agents World Wide Web 4(3), 222–228 (2006)

    Article  Google Scholar 

  10. Gomez-Perez, A., Fernandez-Lopez, M., Corcho, O.: Ontological Engineering: with Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. Advanced Information and Knowledge Processing. Springer, New York (2004). https://doi.org/10.1007/b97353

    Book  Google Scholar 

  11. Hastings, J., et al.: Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies. Front. Neuroinf. 8, 62 (2014)

    Article  Google Scholar 

  12. Hoehndorf, R., Schofield, P.N., Gkoutos, G.V.: The role of ontologies in biological and biomedical research: a functional perspective. Briefings Bioinf. 16(6), 1069–1080 (2015)

    Article  Google Scholar 

  13. Ivascu, T., Manate, B., Negru, V.: A multi-agent architecture for ontology-based diagnosis of mental disorders. In: 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 423–430. IEEE. September 2015

    Google Scholar 

  14. Jensen, M., et al.: The neurological disease ontology. J. Biomed. Semant. 4(1), 42 (2013)

    Article  Google Scholar 

  15. Klein, M.: Combining and Relating Ontologies: an analysis of problems and solutions. In: Ontologies and Information Sharing, vol. 47, May 2001

    Google Scholar 

  16. Malhotra, A., Younesi, E., Gndel, M., Mller, B., Heneka, M.T., Hofmann-Apitius, M.: ADO: a disease ontology representing the domain knowledge specific to Alzheimer’s disease. Alzheimer’s Dement. 10(2), 238–246 (2014)

    Article  Google Scholar 

  17. Mead, C.N.: Data interchange standards in healthcare IT - computable semantic interoperability: now possible but still difficult, do we really need a better mousetrap? J. healthc. Inf. Manage. (JHIM) 20, 71–78 (2006)

    Google Scholar 

  18. Sanchez, E., et al.: A knowledge-based clinical decision support system for the diagnosis of Alzheimer disease. In: 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, pp. 351–357. IEEE. June 2011

    Google Scholar 

  19. Trokanas, N., Cecelja, F.: Ontology evaluation for reuse in the domain of process systems engineering. Comput. Chem. Eng. 85, 177–187 (2016)

    Article  Google Scholar 

  20. Whitwell, J.L., et al.: MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 70(7), 512–520 (2008)

    Article  Google Scholar 

  21. Zekri, F., Bouaziz, R., Turki, E.: A fuzzy-based ontology for Alzheimer’s disease decision support. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE. August 2015

    Google Scholar 

  22. Zhang, X., Bin, H., Ma, X., Moore, P., Chen, J.: Ontology driven decision support for the diagnosis of mild cognitive impairment. Comput. Methods Programs Biomed. 113(3), 781–791 (2014)

    Article  Google Scholar 

Download references

Funding

We thanks to the Ministry of Education, Youth and Sports of the Community of Madrid, and the European Social Fund for a contract to A.G.-V. B. (PEJD-2017-PRE/TIC-4406) in the program of Youth Employment and the Youth Employment Initiative (YEI) 2018.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alba Gomez-Valadés .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gomez-Valadés, A., Martínez-Tomás, R., Rincón-Zamorano, M. (2019). Ontologies for Early Detection of the Alzheimer Disease and Other Neurodegenerative Diseases. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19591-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19590-8

  • Online ISBN: 978-3-030-19591-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics