Multi-State Health Transition Modeling Using Neural Networks

Abstract

This article proposes a new model that combines a neural network with a generalized linear model (GLM) to estimate and predict health transition intensities. The model allows for socioeconomic and lifestyle factors to impact the health transition processes, and captures linear and nonlinear relationships. A key innovation is that the model features transfer learning between different transition rates. It autonomously finds the relationships between factors and the links between the transition processes. 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 thus provide new estimates of the life expectancies for a range of population subgroups. The model can be easily applied to other datasets, and our results confirm that machine learning techniques are promising tools to model insurance risks.