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Applying neural network Poisson regression to predict cognitive score changes

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  • Nader Fallah
  • Arnold Mitnitski
  • Kenneth Rockwood

Abstract

In this study, we combined a Poisson regression model with neural networks (neural network Poisson regression) to relax the traditional Poisson regression assumption of linearity of the Poisson mean as a function of covariates, while including it as a special case. In four simulated examples, we found that the neural network Poisson regression improved the performance of simple Poisson regression if the Poisson mean was nonlinearly related to covariates. We also illustrated the performance of the model in predicting five-year changes in cognitive scores, in association with age and education level; we found that the proposed approach had superior accuracy to conventional linear Poisson regression. As the interpretability of the neural networks is often difficult, its combination with conventional and more readily interpretable approaches under the generalized linear model can benefit applications in biomedicine.

Suggested Citation

  • Nader Fallah & Arnold Mitnitski & Kenneth Rockwood, 2011. "Applying neural network Poisson regression to predict cognitive score changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 2051-2062, November.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2051-2062
    DOI: 10.1080/02664763.2010.545112
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