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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Etchells, T. A., Fernandes, A. S., Jarman, I. H., Fonseca, J. M., & Lisboa, P. J. G. (2008). Stratification of severity of illness indices: A case study for breast cancer prognosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5178 LNAI, pp. 214–221). Springer Verlag. https://doi.org/10.1007/978-3-540-85565-1_27
Mendeley helps you to discover research relevant for your work.