Artificial Intelligence in Medicine, Volume 28

Volume 28, Number 1, May 2003

• Paulo J. G. Lisboa, H. Wong, P. Harris, R. Swindell:
  A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. 1-25
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• Dragan Gamberger, Nada Lavrac:
  Active subgroup mining: a case study in coronary heart disease risk group detection. 27-57
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• Carsten Peterson, Markus Ringnér:
  Analyzing tumor gene expression profiles. 59-74
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• Andreas Alexander Albrecht, Staal A. Vinterbo, Lucila Ohno-Machado:
  An Epicurean learning approach to gene-expression data classification. 75-87
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• Christopher J. James, David Lowe:
  Extracting multisource brain activity from a single electromagnetic channel. 89-104
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• Ralf Schweiger, Simon Hölzer, Dirk Rudolf, Joerg Rieger, Joachim Dudeck:
  Linking clinical data using XML topic maps. 105-115
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Volume 28, Number 2, June 2003

• Ian Cloete, Karl Rohr:
  Knowledge-based neurocomputing in medicine. 117-119
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• Christian W. Omlin, Sean Snyders:
  Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues. 121-140
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• Guido Bologna:
  A model for single and multiple knowledge based networks. 141-163
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• Matthias E. Futschik, Anthony Reeve, Nikola Kasabov:
  Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue. 165-189
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• Robert Wall, Padraig Cunningham, Paul Walsh, Stephen Byrne:
  Explaining the output of ensembles in medical decision support on a case by case basis. 191-206
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• Jürgen Paetz:
  Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions. 207-230
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Volume 28, Number 3, July 2003

• Guy Carrault, Marie-Odile Cordier, Rene Quiniou, Feng Wang:
  Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms. 231-263
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• Frank Dieterle, Silvia Müller-Hagedorn, Hartmut M. Liebich, Günter Gauglitz:
  Urinary nucleosides as potential tumor markers evaluated by learning vector quantization. 265-279
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• Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Ignace Vergote, Dirk Timmerman:
  Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. 281-306
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• Sergio Di Bona, Heinrich Niemann, Gabriele Pieri, Ovidio Salvetti:
  Brain volumes characterisation using hierarchical neural networks. 307-322
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• M. Radespiel-Tröger, Thomas Rabenstein, H. T. Schneider, Berthold Lausen:
  Comparison of tree-based methods for prognostic stratification of survival data. 323-341
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