用于预测入院人数的可解释零膨胀神经网络模型

IF 1.5 Q3 BUSINESS, FINANCE Annals of Actuarial Science Pub Date : 2024-03-26 DOI:10.1017/s1748499524000058
Alex Jose, Angus S. Macdonald, George Tzougas, George Streftaris
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引用次数: 0

摘要

在本文中,我们构建了可解释的零膨胀神经网络模型,用于对美国医疗保险人群及其家属中与呼吸系统疾病相关的入院人数进行建模。具体而言,我们通过考虑零膨胀泊松神经网络(ZIPNN)来示范我们的方法,并采用组合精算神经网络(CANN)方法来开发零膨胀组合精算神经网络(ZIPCANN)模型,用于对入院率进行建模,该模型可适应入院人数数据的过零性质。此外,我们还采用了 LocalGLMnet 方法(Richman & Wüthrich (2023)。Scandinavian Actuarial Journal, 2023(1), 71-95.)来解释 ZIPNN 模型的结果。这有助于分析一些社会人口因素对呼吸系统疾病入院率的影响,同时提高预测性能。作为这项工作的一部分而开发的方法在现实生活中的实用性在于,除了为健康干预措施提供信息的潜力之外,它们还有助于准确的费率设定。
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Interpretable zero-inflated neural network models for predicting admission counts

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.

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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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