Multiple organ failure diagnosis using adverse events and neural networks

ICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems

Volumn , Issue , 2004, Pages 401-408

  Silva, Álvaro ^a   Cortez, Paulo ^b   Santos, Manuel ^b   Gomes, Lopes ^c   Neves, José ^b  

^a HOSPITAL DE SANTO ANTÓNIO   (Portugal)

^b UNIVERSITY OF MINHO   (Portugal)

^c UNIVERSITY OF PORTO   (Portugal)

Author keywords

Classification; Clinical Data Mining; Intensive Care Medicine; Multilayer Perceptrons

Indexed keywords

BIOLOGICAL ORGANS; CARDIOVASCULAR SYSTEM; DATA MINING; DATA REDUCTION; DECISION MAKING; FAILURE ANALYSIS; INFORMATION ANALYSIS; INTENSIVE CARE UNITS; NEUROLOGY; NEUROSURGERY;

BLOOD PRESSURE; ORGAN FAILURE; OXYGEN SATURATION; URINE OUTPUT;

NEURAL NETWORKS;

EID: 8444231665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
2. Cios, K. and Moore, G. (2002). Uniqueness of Medical Data Mining. Artificial Intelligence in Medicine, 26(12): 1-24.
3. Dybowski, R. (2000). Neural Computation in Medicine: Perspectives and Prospects. In et al., H. M., editor, Proceedings of the ANNIMAB-1 Conference (Artificial Neural Networks in Medicine and Biology), pages 26-36. Springer.
4. Essex-Sorlie, D. (1995). Medical Biostatistics & Epidemiology: Examination & Board Review. McGrawHill/Appleton & Lange, International edition.
5. Flexer, A. (1996). Statistical evaluation of neural networks experiments: Minimum requirements and current practice. In Proceedings of the 13th European Meeting on Cybernetics and Systems Research, volume 2, pages 1005-1008, Vienna, Austria.
6. Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining. MIT Press, Cambridge, MA.
7. Haykin, S. (1999). Neural Networks - A Compreensive Foundation. Prentice-Hall, New Jersey, 2nd edition.
8. Kohavi, R. and Provost, F. (1998). Glossary of Terms. Machine Learning, 30(2/3):271-274.
9. Prechelt, L. (1998). Early Stopping - but when? In: Neural Networks: Tricks of the trade, Springer Verlag, Heidelberg.
10. Riedmiller, M. (1994). Supervised Learning in Multilayer Perceptrons - from Backpropagation to Adaptive Learning Techniques. Computer Standards and Interfaces, 16.
11. Rocha, M., Cortez, P., and Neves, J. (2003). Evolutionary Neural Network Learning. In Pires, P. and Abreu, S., editors, Progress in Artificial Intelligence, EPIA 2003 Proceedings, LNAI 2902, pages 24-28, Beja, Portugal. Springer.
12. Santos, M., Neves, J., Abelha, A., Silva, A., and Rua, F. (2002). Augmented Data Mining Over Clinical Databases Using Learning Classifier System. In Proceedings of the 4th Int. Conf. on Enterprise Information Systems - ICEIS 2002, pages 512-516, Ciudad Real, Spain.
13. Teres, D. and Pekow, P. (2000). Assessment data elements in a severity scoring system (Editorial). Intensive Care Med, 26:263-264.
14. Vincent, J., Moreno, R., Takala, J., Willatss, S., Mendonca, A. D., Bruining, H., Reinhart, C., Suter, P., and Thijs, L. (1996). The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction / failure. Intensive Care Med, 22:707-710.
15. Witten, I. and Frank, E. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA.