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The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models.
Alabdallah, Abdallah; Ohlsson, Mattias; Pashami, Sepideh; Rögnvaldsson, Thorsteinn.
Afiliación
  • Alabdallah A; Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden. Electronic address: abdallah.alabdallah@hh.se.
  • Ohlsson M; Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden; Department of Astronomy and Theoretical Physics, Lund University, Sweden.
  • Pashami S; Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden; RISE Research Institutes of Sweden, Stockholm, Sweden.
  • Rögnvaldsson T; Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
Artif Intell Med ; 148: 102781, 2024 02.
Article en En | MEDLINE | ID: mdl-38325926
ABSTRACT
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de Supervivencia / Redes Neurales de la Computación / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de Supervivencia / Redes Neurales de la Computación / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos