Multistate health transition modeling using neural networks

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<title>Multistate health transition modeling using neural networks</title>
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<namePart>Wang, Qiqi </namePart>
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<namePart>Hanewald, Katja</namePart>
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<namePart>Wang, Xiaojun</namePart>
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<abstract displayLabel="Summary">This article proposes a new model that combines a neural network with a generalized linear model (GLM) to estimate and predict health transition intensities. We introduce neural networks to health transition modeling to incorporate socioeconomic and lifestyle factors and to allow for linear and nonlinear links between these variables. We use transfer learning to link the models for different health transitions and improve the model estimation for health transitions with limited data. We apply the model to individual-level data from the Chinese Longitudinal Healthy Longevity Survey from 1998 to 2018. The results show that our model performs better in estimation and prediction than standalone GLM and neural network models. We provide new estimates of the life expectancies for a range of population subgroups. We also describe a wide range of possible applications for further health-related research, including risk prediction using health claim data and mortality prediction based on individual-level mortality data.

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<note type="statement of responsibility">Qiqi Wang, Katja Hanewald, Xiaojun Wang</note>
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<topic>Mortalidad</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20210020971">
<topic>Factores psicosociales</topic>
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<title>The Journal of risk and insurance</title>
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<publisher>Nueva York : The American Risk and Insurance Association, 1964-</publisher>
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<identifier type="issn">0022-4367</identifier>
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<text>09/05/2022 Volumen 89 Número 2 - mayo 2022 , p. 475-504</text>
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