Reports Summarize Risk Management Findings from Renmin University of China (Multistate Health Transition Modeling Using Neural Networks): Risk Management

2021 OCT 29 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Fresh data on Risk Management are presented in a new report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “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.”

Financial supporters for this research include Australian Research Council, Ministry of Education, China.

The news correspondents obtained a quote from the research from the Renmin University of China, “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.”

According to the news reporters, the research concluded: “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.”

This research has been peer-reviewed.

For more information on this research see: Multistate Health Transition Modeling Using Neural Networks. Journal of Risk & Insurance, 2021. Journal of Risk & Insurance can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Journal of Risk & Insurance - http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1539-6975)

Our news journalists report that additional information may be obtained by contacting Xiaojun Wang, Renmin University of China, School of Statistics, 59 Zhongguancun St, Beijing 100872, People’s Republic of China. Additional authors for this research include Qiqi Wang and Katja Hanewald.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1111/jori.12364. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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