Multi-State Health Transition Modeling Using Neural Networks
ARC Centre of Excellence in Population Ageing Research Working Paper 2020/25
44 Pages Posted: 29 Oct 2020 Last revised: 20 Aug 2021
Date Written: September 16, 2020
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 relationships 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 1998–2018. 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.
Keywords: Neural networks, Transfer learning, Multi-state health transitions
JEL Classification: C13, C53, G22, J11
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