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International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2013: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 125–136Cite as

Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

Abstract

The hazard function plays an important role in the study of disease dynamics in survival analysis. Longer follow-up for various kinds of cancer, particularly breast cancer, has made it possible the observation of complex shapes of the hazard function of occurrence of metastasis and death. The identification of the correct hazard shape is important both for formulation and support of biological hypotheses on the mechanism underlying the disease.

In this paper we propose the use of a neural network to model the shape of the hazard function in time in dependence of covariates extending the piecewise exponential model. The use of neural networks accommodates a greater flexibility in the study of the hazard shape.

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Authors and Affiliations

  1. Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy

    Marco Fornili, Federico Ambrogi, Patrizia Boracchi & Elia Biganzoli

  2. Unit of Medical Statistics, Biometry and Bioinformatics, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy

    Elia Biganzoli

Authors
  1. Marco Fornili
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  2. Federico Ambrogi
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  3. Patrizia Boracchi
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  4. Elia Biganzoli
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Corresponding author

Correspondence to Marco Fornili .

Editor information

Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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Fornili, M., Ambrogi, F., Boracchi, P., Biganzoli, E. (2014). Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-09042-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09041-2

  • Online ISBN: 978-3-319-09042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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