Stratification of Severity of Illness Indices: A Case Study for Breast Cancer Prognosis

Authors:
Terence A. Etchells;Ana S. Fernandes;Ian H. Jarman;José M. Fonseca;Paulo J. Lisboa
Affiliations:
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK L3 3AF;Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa,;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK L3 3AF;Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa,;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK L3 3AF
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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Abstract

Prognostic modelling involves grouping patients by risk of adverse outcome, typically by stratifying a severity of illness index obtained from a classifier or survival model. The assignment of thresholds on the risk index depends of pairwise statistical significance tests, notably the log-rank test. This paper proposes a new methodology to substantially improve the robustness of the stratification algorithm, by reference to a statistical and neural network prognostic study of longitudinal data from patients with operable breast cancer.