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Hospital Admission Rates in São Paulo, Brazil - Lee-Carter model vs. neural networks

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  • Rodolfo Monfilier Peres
  • Onofre Alves Simões

Abstract

In Brazil, hospital admissions account for nearly 50% of the total cost of health insurance claims, while representing only 1% of total medical procedures. Therefore, modeling hospital admissions is useful for insurers to evaluate costs in order to maintain their solvency. This article analyzes the use of the Lee-Carter model to predict hospital admissions in the state of São Paulo, Brazil, and contrasts it with the Long Short Term Memory (LSTM) neural network. The results showed that the two approaches have similar performance. This was not a disappointing result, on the contrary: from now on, future work can further test whether LSTM models are able to give a better result than Lee-Carter, for example by working with longer data sequences or by adapting the models.

Suggested Citation

  • Rodolfo Monfilier Peres & Onofre Alves Simões, 2024. "Hospital Admission Rates in São Paulo, Brazil - Lee-Carter model vs. neural networks," Working Papers REM 2024/0349, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp03492024
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    File URL: https://rem.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_0349_2024.pdf
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    References listed on IDEAS

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    1. Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
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    More about this item

    Keywords

    Hospital Admissions; Lee-Carter; Neural Networks; LSTM; Brazil.;
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