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

Rodolfo Monfilier Peres and Onofre Alves Simões

No 2024/0349, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

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.

Keywords: Hospital Admissions; Lee-Carter; Neural Networks; LSTM; Brazil. (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-big and nep-cmp
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