Skip to main content

Abstract

Once kidney disease is exposed, the presence or degree of kidney dysfunction and its progression are assessed, and the underlying syndrome may be diagnosed. Although the patient‘s history and corporeal examination may be useful, some key information is obtained from valuation of the Glomerular Filtration Rate, and analysis of the urinary sediment. On the one hand, Chronic Kidney Diseases (CKDs) depicts anomalous kidney function and/or its makeup. On the other hand, there is evidence that treatment may avoid or delay the progression of CKDs, either by reducing and prevent the development of complications, or by reducing the risk of CardioVascular Illnesses. Acute Renal Failure (ARF) can occur over hours to days based on the underlying mechanism of injury and relative health of the individual. ARF is often reversible if it is recognized early and treated promptly. This is the reason behind our compromise in presenting this work, that aims at the development of an early diagnosis system to monitor the occurrence of the disease, and therefore to allow one to act proactively.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 369.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Levey, A.S., Coresh, J.: Chronic kidney disease. Lancet 379, 165–180 (2012)

    Article  Google Scholar 

  2. Chronic Kidney Disease Platform, http://gid.min-saude.pt/irc.php?lang=en

  3. Hemmelgarn, B.R., Manns, B.J., Lloyd, A., James, M.T., Klarenbach, S., Quin, R.R., Wiebe, N., Tonelli, M.: for the Alberta Kidney Disease Network: Relation between kidney function, proteinuria, and adverse outcomes. Journal of American Medical Association 303, 423–429 (2010)

    Article  Google Scholar 

  4. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group: KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International Supplements 3, 1–150 (2013)

    Google Scholar 

  5. Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. NICE clinical guideline 181, National Institute for Health and Care Excellence (2014), http://www.nice.org.uk/guidance/cg181/resources/guidance-lipid-modification-cardiovascular-risk-assessment-and-the-modification-of-blood-lipids-for-the-primary-and-secondary-prevention-of-cardiovascular-disease-pdf

  6. Praga, M., Hernandez, E., Herrero, J.C., Morales, E., Revilla, Y., Diaz-Gonzalez, R., Rodicio, J.L.: Influence of Obesity on the Appearance of Proteinuria and Renal Insufficiency after Unilateral Nephrectomy. Kidney International 58, 2111–2118 (2000)

    Article  Google Scholar 

  7. Locatelli, F., Aljama, P., Bárány, P., Canaud, B., Carrera, F., Eckardt, K.U., Hörl, W.H., Macdougal, I.C., Macleod, A., Wiecek, A., Cameron, S.: Revised European Best Practice Guidelines for the Management of Anaemia in Patients with Chronic Renal Failure. Nephrology Dialysis Transplantation 19(suppl. 2), ii44–ii47 (2004)

    Google Scholar 

  8. Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R.L., Pottmyer, J.J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the Fifth Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)

    Chapter  Google Scholar 

  9. Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 160–169. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Cortez, P., Rocha, M., Neves, J.: Evolving Time Series Forecasting ARMA Models. Journal of Heuristics 10, 415–429 (2004)

    Article  Google Scholar 

  11. Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)

    Google Scholar 

  12. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Logic Programming – Proceedings of the Fifth International Conference and Symposium, pp. 1070–1080 (1988)

    Google Scholar 

  13. Pereira, L.M., Anh, H.T.: Evolution prospection. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds.) New Advances in Intelligent Decision Technologies. Studies in Computational Intelligence, vol. 199, pp. 51–63. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Halpern, J.: Reasoning about uncertainty. MIT Press, Massachusetts (2005)

    MATH  Google Scholar 

  15. Kovalerchuck, B., Resconi, G.: Agent-based uncertainty logic network. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Barcelona, pp. 596–603 (2010)

    Google Scholar 

  16. Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh, A. (eds.) Proceedings of AI-2003 (Research and Developments in Intelligent Systems XX), pp. 309–321. Springer, London (2003)

    Google Scholar 

  17. Machado, J., Abelha, A., Novais, P., Neves, J., Neves, J.: Quality of Service in healthcare units. International Journal of Computer Aided Engineering and Technology 2, 436–449 (2010)

    Article  Google Scholar 

  18. Liu, Y., Sun, M.: Fuzzy optimization BP neural network model for pavement performance assessment. In: 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, pp. 18–20 (2007)

    Google Scholar 

  19. World Health Organization: Obesity and overweight. Fact Sheet Number 311, http://www.who.int/mediacentre/factsheets/fs311/en/

  20. Caldeira, A.T., Arteiro, J., Roseiro, J., Neves, J., Vicente, H.: An Artificial Intelligence Approach to Bacillus amyloliquefaciens CCMI 1051 Cultures: Application to the Production of Antifungal Compounds. Bioresource Technology 102, 1496–1502 (2011)

    Article  Google Scholar 

  21. Vicente, H., Dias, S., Fernandes, A., Abelha, A., Machado, J., Neves, J.: Prediction of the Quality of Public Water Supply using Artificial Neural Networks. Journal of Water Supply: Research and Technology – AQUA 61, 446–459 (2012)

    Article  Google Scholar 

  22. Salvador, C., Martins, M.R., Vicente, H., Neves, J., Arteiro, J.M., Caldeira, A.T.: Modelling Molecular and Inorganic Data of Amanita ponderosa Mushrooms using Artificial Neural Networks. Agroforestry Systems 87, 295–302 (2013)

    Article  Google Scholar 

  23. Carneiro, D., Novais, P., Andrade, F., Zeleznikow, J., Neves, J.: Using Case-Based Reasoning and Principled Negotiation to provide decision support for dispute resolution. Knowledge and Information Systems 36, 789–826 (2013)

    Article  Google Scholar 

  24. Mendes, R., Kennedy, J., Neves, J.: The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José Neves .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Neves, J., Martins, M.R., Vicente, H., Neves, J., Abelha, A., Machado, J. (2015). An Assessment of Chronic Kidney Diseases. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16486-1_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16485-4

  • Online ISBN: 978-3-319-16486-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics