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.
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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: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_27
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DOI: https://doi.org/10.1007/978-3-540-85565-1_27
Publisher Name: Springer, Berlin, Heidelberg
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