Publicação
Hospital admission rates in São Paulo, Brazil : Lee-Carter model vs. neural networks
| Resumo: | 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. |
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| Autores principais: | Peres, Rodolfo Monfilier |
| Outros Autores: | Simões, Onofre Alves |
| Assunto: | Hospital Admissions Lee-Carter Neural Networks LSTM Brazil |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | working paper |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | 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. |
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