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Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models

Författare

  • Abdallah Alabdallah
  • Mattias Ohlsson
  • Sepideh Pashami
  • Thorsteinn Rögnvaldsson

Summary, in English

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.

Avdelning/ar

  • LU profilområde: Naturlig och artificiell kognition
  • Centrum för miljö- och klimatvetenskap (CEC)
  • eSSENCE: The e-Science Collaboration
  • Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)

Publiceringsår

2024-02

Språk

Engelska

Publikation/Tidskrift/Serie

Artificial Intelligence in Medicine

Volym

148

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Probability Theory and Statistics

Nyckelord

  • Concordance Index
  • Evaluation metric
  • Survival analysis
  • Variational encoder–decoder

Status

Published

Forskningsgrupp

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Övrigt

  • ISSN: 0933-3657