Thrombophilia Screening - An Artificial Neural Network Approach

João Vilhena, M. Rosário Martins, Henrique Vicente, Luís Nelas, José Machado, José Neves


Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.


  1. Abelha, V., Vicente, H., Machado, J., Neves, J., 2014. An assessment on the length of hospital stay through artificial neural networks. In 9th International Conference on Knowledge, Information and Creativity Support Systems (to appear).
  2. Agrawal, N., Kumar, S., Puneet, Khanna, R., Shukla, J., Khanna, A., 2009. Activated Protein C Resistance in Deep Venous Thrombosis. Annals of Vascular Surgery, 23: 364-366.
  3. Cafolla, A., D'Andrea, G., Baldacci, E., Margaglione, M., Mazzucconi, M.G., Foa, R., 2011. Hereditary protein C deficiency and thrombosis risk: genotype and phenotype relation in a large Italian family. European Journal of Haematology, 88: 336-339.
  4. Caldeira, A.T., Arteiro, J., Roseiro, J. Neves, J., Vicente, H., 2011. An Artificial Intelligence Approach to Bacillus amyloliquefaciens CCMI 1051 Cultures: Application to the Production of Antifungal Compounds. Bioresource Technology, 102: 1496-1502.
  5. Carneiro, D., Novais, P., Andrade, F., Zeleznikow, J., Neves, J., 2013. Using Case-Based Reasoning and Principled Negotiation to provide decision support for dispute resolution. Knowledge and Information Systems, 36: 789-826.
  6. Cohen, A., Agnelli, G., Anderson, F., Arcelus, J., Bergqvist D., Brecht, J., Greer, I., Heit, J., Hutchinson, J., Kakkar, A., Mottier, D., Oger, E., Samama, M., Spannagl, M., 2007. Venous thromboembolism (VTE) in Europe. The number of VTE events and associated morbidity and mortality. Thrombosis and Haemostasis, 98: 756-764.
  7. Cortez, P., Rocha, M., Neves, J., 2004. Evolving Time Series Forecasting ARMA Models. Journal of Heuristics, 10: 415-429.
  8. East, A., Wakefield, T., 2010. What is the optimal duration of treatment for DVT? An update on evidence-based medicine of treatment for DVT. Seminars in Vascular Surgery, 23: 182-191.
  9. Favaloro, E., McDonald, D., Lippi, G., 2009. Laboratory investigations of thrombophilia: the good, the bad and the ugly. Seminars in Thrombosis and Hemostasis, 35: 695-710.
  10. Freire, L., Roche, A., Mangin, J-F., 2002. What is the best similarity measure for motion correction in fMRI time series?. IEEE Transactions on Medical Imaging, 21: 470-484.
  11. Gelfond M., Lifschitz V., 1988. The stable model semantics for logic programming. In Logic Programming - Proceedings of the Fifth International Conference and Symposium, 1070-1080.
  12. Goldhaber, S., 2010. Risk factors for venous thromboembolism. Journal of the American College of Cardiology, 56: 1-7.
  13. Haykin, S., 2008. Neural Networks and Learning Machines. New York: Prentice Hall.
  14. Heit, J., O'Fallon, W., Petterson, T., Lohse, C., Silverstein, M., Mohr, D., Melton III, L., 2002. Relative impact of risk factors for deep vein thrombosis and pulmonary embolism: a population-based study. Archives of Internal Medicine, 162: 1245-1248.
  15. Hong, T., Hart, K., Soh, L-K, Samal, A., 2014. Using spatial data support for reducing uncertainty in geospatial applications. Geoinformatica, 18: 63-92.
  16. Kakas A., Kowalski R. & Toni F., 1998. The role of abduction in logic programming. In Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 5, 235-324.
  17. Hommerson, A., P. Lucas, van Bommel, P., 2008. Checking the quality of clinical guidelines using automated reasoning tools. Theory and Practice of Logic Program, 8: 611-641.
  18. Hunter, G., 1999. Managing uncertainty in GIS. In Geographical Information Systems, New York: J. Wiley & Sons, 633-641.
  19. Li, R., Bhanu, B., Ravishankar, C., Kurth, M., Ni, J., 2007. Uncertain spatial data handling: Modeling, indexing and query. Computers & Geosciences, 33: 42-61.
  20. Liao, T., 2005. Clustering of time series data - a survey. Pattern Recognition, 38: 1857-1874.
  21. Liu, Y., Sun, M., 2007. Fuzzy optimization BP neural network model for pavement performance assessment. In 2007 IEEE international conference on grey systems and intelligent services, Nanjing, China, 18-20.
  22. Lucas P., 2003. Quality checking of medical guidelines through logical abduction. In Proceedings of AI-2003, Springer: London, 309-321.
  23. Machado J., Abelha A., Novais P., Neves J., 2010. Quality of service in healthcare units. International Journal of Computer Aided Engineering and Technology, 2: 436-449.
  24. Mendes, R., Kennedy, J., Neves, J., 2004. The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Transactions on Evolutionary Computation, 8: 204-210.
  25. Mondal, R., Nandi, M., Dhibar, T., 2010. Protein C and Protein S Deficiency Presenting as Deep Venous Thrombosis. Indian Pediatrics, 47:188-189.
  26. Neves J., 1984. A logic interpreter to handle time and negation in logic data bases. In Proceedings of the 1984 annual conference of the ACM on the fifth generation challenge, 50-54.
  27. Neves J., Machado J., Analide C., Abelha A., Brito L., 2007. The halt condition in genetic programming. In Progress in Artificial Intelligence - Lecture Notes in Computer Science, Volume 4874, 160-169.
  28. Pereira L. Anh H., 2009. Evolution prospection. In New Advances in Intelligent Decision Technologies - Results of the First KES International Symposium IDT, 51-64.
  29. Pereira, S., Gomes, S., Vicente, H., Ribeiro, J., Abelha, A., Novais, P., Machado, J., Neves, J., 2014. An Artificial Neuronal Network Approach to Diagnosis of Attention Deficit Hyperactivity Disorder. In Proceedings of 2014 IEEE International Conference on Imaging Systems and Techniques (IST 2014), Institute of Electrical and Electronics Engineers, Inc.: New Jersey, 410-415.
  30. Reitsma, P., Rosendaal, F., 2007. Past and future of genetic research in thrombosis. Journal of Thrombosis and Haemostasis, 5: 264-269.
  31. Rodrigues, B., Gomes, S., Vicente, H., Abelha, A., Novais, P., Machado, J., Neves, J., 2014. Systematic coronary risk evaluation through artificial neural networks based systems. In 27th International Conference on Computer Applications in Industry and Engineering (to appear).
  32. Rosendaal, F., 1999. Venous thrombosis: a multicausal disease. Lancet, 353: 1167-1173.
  33. Sacher, R. A. 1999. Thrombophilia: a Genetic Predisposition to Thrombosis. Transactions of the American Clinical and Climatological Association, 110: 51-61.
  34. Salvador, C., Martins, M.R., Vicente, H., Neves, J., Arteiro J., Caldeira, A.T., 2013. Modelling Molecular and Inorganic Data of Amanita ponderosa Mushrooms using Artificial Neural Networks. Agroforestry Systems, 87: 295-302.
  35. Schneider, M., 1999. Uncertainty management for spatial data in databases: Fuzzy spatial data types. In Lecture Notes in Computer Science, Volume 1651, 330-351.
  36. Spiezia, L., Campello, E., Bom, M., Tison, T., Milan, M., Simioni, P., Prandoni, P., 2013. ABO blood groups and the risk of venous thrombosis in patients with inherited thrombophilia. Blood Transfusion, 11:250-253.
  37. Vicente, H., Dias, S., Fernandes, A., Abelha, A., Machado, J., Neves, J., 2012. Prediction of the Quality of Public Water Supply using Artificial Neural Networks. Journal of Water Supply: Research and Technology - AQUA, 61: 446-459.
  38. WHO, 2014. Obesity and overweight. Fact Sheet Number 311, World Health Organization. Accessed August 10, 2014.
  39. Zhang, J., Goodchild, M., 2002. Uncertainty in geographical information. New York: CRC press.

Paper Citation

in Harvard Style

Vilhena J., Rosário Martins M., Vicente H., Nelas L., Machado J. and Neves J. (2015). Thrombophilia Screening - An Artificial Neural Network Approach . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 51-59. DOI: 10.5220/0005197500510059

in Bibtex Style

author={João Vilhena and M. Rosário Martins and Henrique Vicente and Luís Nelas and José Machado and José Neves},
title={Thrombophilia Screening - An Artificial Neural Network Approach},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Thrombophilia Screening - An Artificial Neural Network Approach
SN - 978-989-758-068-0
AU - Vilhena J.
AU - Rosário Martins M.
AU - Vicente H.
AU - Nelas L.
AU - Machado J.
AU - Neves J.
PY - 2015
SP - 51
EP - 59
DO - 10.5220/0005197500510059