Interpretable zero-inflated neural network models for predicting admission counts

Alex Jose, Angus Smith Macdonald, George Tzougas, George Streftaris

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Abstract

In this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman & Wüthrich (2023). Scandinavian Actuarial Journal, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.
Original languageEnglish
JournalAnnals of Actuarial Science
Early online date26 Mar 2024
DOIs
Publication statusE-pub ahead of print - 26 Mar 2024

Keywords

  • Predictive modeling
  • actuarial
  • admission rates
  • morbidity
  • neural network
  • zero-inflated neural network

ASJC Scopus subject areas

  • Economics and Econometrics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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