Volume 47 - Article 8 | Pages 199–232
Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth
By Andrea Nigri, Susanna Levantesi, Jose Manuel Aburto
References
Aburto, J.M., Beltrán-Sánchez, H., García-Guerrero, V.M., and Canudas-Romo, V. (2016). Homicides in Mexico reversed life expectancy gains for men and slowed them for women, 2000-10. Health Affairs 35(1): 88–95.
Aburto, J.M., Kashyap, R., Schöley, J., Angus, C., Ermisch, J., Mills, M., and Dowd, J.B. (2021). Estimating the burden of the covid-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: A population-level analysis. Journal of Epidemiology and Community Health .
Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8): 1798–1828.
García, J. and Aburto, J.M. (2019). The impact of violence on Venezuelan life expectancy and lifespan inequality. International Journal of Epidemiology 48(5): 1593–1601.
Glorot, X., Bordes, A., and Bengio, Y. (eds.) (2011). Deep sparse rectifier neural networks. Fort Lauderdale, FL, USA: JMLR (Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics).
Hainaut, D. (2018). A neural-network analyser for mortality forecast. ASTIN Bulletin 48(2): 481–508.
He, X. and Ng, P. (1999). Cobs: Qualitatively constrained smoothing via linear programming. Computational Statistics 14: 315–337.
Hinton, G., Srivastava, N., and Swersky, K. (2013). Neural networks for machine learning. lecture 6a: Overview of mini-batch gradient descent. Department of Computer Science University of Toronto.
HMD (2021). Berkeley: University of California and Max Planck Institute for Demographic Research.
Ho, J. and Hendi, A. (2018). Recent trends in life expectancy across high income countries: Retrospective observational study. BMJ 362(k2562).
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521(7553): 436–444.
Lee, R. (2006). Mortality forecasts and linear life expectancy trends. Perspectives on mortality forecasting. The linear rise in life expectancy: History and prospects. Social Insurance Studies 3: 19–39.
Lee, R. and Carter, L. (1992). Modeling and forecasting us mortality. Journal of the American Statistical Association 87: 659–671.
Lee, R. and Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography 38(6): 537–549.
Levantesi, S., Nigri, A., and Piscopo, G. (2022). Clustering-based simultaneous forecasting of life expectancy time series through long-short term memory neural networks. International Journal of Approximate Reasoning 140: 282–297.
Li, N., Lee, R., and Gerland, P. (2013). Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography 50(6): 2037–2051.
Luy, M. (2003). Causes of male excess mortality: Insights from cloistered populations. Population and Development Review 29(4): 647–676.
Marino, M., Levantesi, S., and Nigri, A. (2022). A neural approach to improve the Lee-Carter mortality density forecasts. North American Actuarial Journal .
Mehta, N.K., Abrams, L.R., and Myrskylä, M. (2020). US life expectancy stalls due to cardiovascular disease, not drug deaths. Proceedings of the National Academy of Sciences 117(13): 6998–7000.
Montavon, G., Samek, W., and Múller, K. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing 73: 1–15.
Nigri, A., Barbi, E., and Levantesi, S. (2021). The relationship between longevity and lifespan variation. Statistical Methods and Applications .
Nigri, A., Levantesi, S., and Marino, M. (2021). Life expectancy and lifespan disparity forecasting: a long short-term memory approach. Scandinavian Actuarial Journal 2021(2): 110–133.
Nigri, A., Levantesi, S., Marino, M., Scognamiglio, S., and Perla, F. (2019). A deep learning integrated Lee-Carter model. Risks 7(1).
Oeppen, J. and Vaupel, J.W. (2002). Broken limits to life expectancy. Science 296(5570): 1029–1031.
Pascariu, M.D., Basellini, U., Aburto, J.M., and Canudas-Romo, V. (2020). The linear link: Deriving age-specific death rates from life expectancy. Risks 8(4).
Pascariu, M.D., Canudas-Romo, V., and Vaupel, J.W. (2018). The double-gap life expectancy forecasting model. Insurance: Mathematics and Economics 78: 339–350.
Perla, F., Richman, R., Scognamiglio, S., and Wüthrich, M. (2021). Time-series forecasting of mortality rates using deep learning. Scandinavian Actuarial Journal 7: 572–598.
Raftery, A.E., Chunn, J.L., Gerland, P., and Ševčíková, H. (2013). Bayesian probabilistic projections of life expectancy for all countries. Demography 50(3): 777–801.
Richman, R. (2020). AI in actuarial science – a review of recent advances - part 1. Annals of Actuarial Science : 1–23.
Richman, R. (2021). Mind the gap - safely incorporating deep learning models into the actuarial toolkit. SSRN 7.
Richman, R. and Wüthrich, M. (2021). A neural network extension of the Lee-Carter model to multiple populations. Annals of Actuarial Science 15(2): 346–366.
Rumelhart, D., Hinton, G., and Williams, R. (1986). Learning representations by back-propagating errors. Nature 323.
Scognamiglio, S. (2022). Calibrating the Lee-Carter and Poisson Lee-Carter via neural networks. ASTIN Bulletin 52(2): 519–561.
Ševčíková, H., Li, N., Kantorová, V., Gerland, P., and Raftery, A.E. (2016). Age-specific mortality and fertility rates for probabilistic population projections. In: Dynamic Demographic Analysis. Springer Series on Demographic Methods and Population Analysis: 285–310.
Torri, T. and Vaupel, J.W. (2012). Forecasting life expectancy in an international context. International Journal of Forecasting 28(2): 519–531.
United Nations (2019). World Population Prospects 2019: Methodology of the United Nations population estimates and projections. Department of Social Affairs.