Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Using sequences of life-events to predict human lives

A preprint version of the article is available at arXiv.


Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A schematic individual-level data representation for the life2vec model.
Fig. 2: Two-dimensional projection of the concept space (using PaCMAP).
Fig. 3: Performance of models on the mortality prediction task quantified with the mean C-MCC with 95% confidence interval.
Fig. 4: Representation of life-sequences conditioned on mortality predictions.
Fig. 5: Performance evaluation for the personality nuances task.

Similar content being viewed by others

Data availability

The data used in this study are not publicly available due to Danish Data Protection regulations. Access to the data can be obtained via Statistics Denmark for Researchers in accordance with the rules of Statistics Denmark’s Research Scheme: Source data are provided with this paper.

Code availability

The source code for the data processing, life2vec training, statistical analysis and visualization is available on GitHub at (ref. 82). The model weights, experiment logs and associated model outputs can be obtained in accordance with the rules of Statistics Denmark’s Research Scheme:


  1. Mansfield, L. A. et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning. NPJ Clim. Atmos. Sci. 3, 44 (2020).

    Article  Google Scholar 

  2. Alali, Y., Harrou, F. & Sun, Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci. Rep. 12, 2467 (2022).

    Article  Google Scholar 

  3. Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (PublicAffairs, 2019).

  4. Weber, M. The Theory of Social and Economic Organization (Simon & Schuster, 2009).

  5. Salganik, M. J. et al. Measuring the predictability of life outcomes with a scientific mass collaboration. Proc. Natl Acad. Sci. USA 117, 8398–8403 (2020).

    Article  Google Scholar 

  6. Lynge, E., Sandegaard, J. L. & Rebolj, M. The Danish National Patient Register. Scand. J. Public Health 39, 30–33 (2011).

    Article  Google Scholar 

  7. Pedersen, C. B. The Danish civil registration system. Scand. J. Public Health 39, 22–25 (2011).

    Article  Google Scholar 

  8. Salganik, M. J. Bit by Bit: Social Research in the Digital Age (Princeton Univ. Press, 2019).

  9. Grimmer, J., Roberts, M. E. & Stewart, B. M. Text as Data: A New Framework for Machine Learning and the Social Sciences (Princeton Univ. Press, 2022).

  10. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (ed. O’Conner L.) 770–778 (IEEE, 2016).

  11. Silver, D. et al. Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    Article  Google Scholar 

  12. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article  Google Scholar 

  13. Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5999–6009 (2017).

    Google Scholar 

  14. Brown, T. et al. Language models are few-shot learners. Proc. NeurIPS 33, 1877–1901 (2020).

    Google Scholar 

  15. Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Sci. Rep. 11, 321 (2021).

    Article  Google Scholar 

  16. Li, Y. et al. BEHRT: transformer for electronic health records. Sci. Rep. 10, 7155 (2020).

    Article  Google Scholar 

  17. Bojesomo, A., Al-Marzouqi, H. & Liatsis, P. Spatiotemporal vision transformer for short time weather forecasting. In Proc. 2021 IEEE International Conference on Big Data (Big Data) (eds. Chen Y. et al.) 5741–5746 (IEEE, 2021).

  18. Huang, C.-Z. A. et al. Music transformer: generating music with long-term structure. Preprint at (2023).

  19. Vafa, K. et al. CAREER: Economic prediction of labor sequence data under distribution shift. In NeurIPS 2022 Workshop DistShift Spotlight (2022).

  20. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. Proc. NAANCL Hum. Lang. Tech. 1, 4171–4186 (2019).

    Google Scholar 

  21. Choromanski, K. M. et al. Rethinking attention with performers. Preprint at (2023).

  22. Kozlowski, A. C., Taddy, M. & Evans, J. A. The geometry of culture: analyzing the meanings of class through word embeddings. Am. Sociol. Rev. 84, 905–949 (2019).

    Article  Google Scholar 

  23. Pilehvar, M. T. & Camacho-Collados, J. Embeddings in natural language processing: theory and advances in vector representations of meaning. Synth. Lect. Hum. Lang. Technol. 13, 1–175 (2020).

    Google Scholar 

  24. Arbejdsmarkedsregnskab (Danmarks Statistik, 2022);

  25. International Standard Classification of Occupations: ISCO-08 (International Labour Office, 2012).

  26. Dansk Branchekode 2007: DB07 (Danish Industrial Classification of All Economic Activities 2007) v3 edn (Danmarks Statistik, 2015).

  27. International Classification of Diseases, 10th Revision (ICD-10) (World Health Organization, 1994).

  28. Yadav, P., Steinbach, M., Kumar, V. & Simon, G. Mining electronic health records (EHRS) a survey. ACM Comput. Surv. 50, 1–40 (2018).

    Article  Google Scholar 

  29. Han, Z., Zhao, J., Leung, H., Ma, K. F. & Wang, W. A review of deep learning models for time series prediction. IEEE Sens. J. 21, 7833–7848 (2019).

    Article  Google Scholar 

  30. Moncada-Torres, A., van Maaren, M. C., Hendriks, M. P., Siesling, S. & Geleijnse, G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci. Rep. 11, 6968 (2021).

    Article  Google Scholar 

  31. Rogers, A., Kovaleva, O. & Rumshisky, A. A primer in BERTology: what we know about how BERT works. Trans. Assoc. Comput. Ling. 8, 842–866 (2021).

    Google Scholar 

  32. Kazemi, S. M. et al. Time2Vec: learning a vector representation of time. Preprint at (2023).

  33. Bachlechner, T., Majumder, B. P., Mao, H., Cottrell, G. & McAuley, J. ReZero is all you need: fast convergence at large depth. Proc. Conf. Uncertainty Artif. Intell. 161, 1352–1361 (2021).

    Google Scholar 

  34. Ramachandran, P., Zoph, B. & Le, Q. V. Searching for activation functions. Preprint at (2023).

  35. Nguyen, T. Q. & Salazar, J. Transformers without tears: improving the normalization of self-attention. Proc. 16th International Conference on Spoken Language Translation (eds Niehues, J. et al.) 2019.iwslt-1.17 (ACL, 2019).

  36. Pappas, N., Miculicich, L. & Henderson, J. Beyond weight tying: learning joint input-output embeddings for neural machine translation. Proc. Third Conference on Machine Translation (eds Borar, O. et al.) W18-6308 (ACL, 2018).

  37. Kanai, S., Fujiwara, Y., Yamanaka, Y. & Adachi, S. Sigsoftmax: reanalysis of the softmax bottleneck. Proc. NeurIPS (eds Bengio S. et al.) 31, 286–296 (2018).

  38. Wang, Y., Huang, H., Rudin, C. & Shaposhnik, Y. Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP and PaCMAP for data visualization. JMLR 22, 9129–9201 (2021).

    MathSciNet  Google Scholar 

  39. Naemi, A. et al. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open 11, e052663 (2021).

    Article  Google Scholar 

  40. Jiang, L., Li, D., Wang, Q., Wang, S. & Wang, S. Improving positive unlabeled learning: practical AUL estimation and new training method for extremely imbalanced data sets. Preprint at (2020).

  41. Wang, C., Pu, J., Xu, Z. & Zhang, J. Asymmetric loss for positive-unlabeled learning. In Proc. 2021 IEEE International Conference on Multimedia and Expo (ICME) 1–6 (IEEE, 2021).

  42. Hansen, A. V., Mortensen, L. H., Ekstrøm, C. T., Trompet, S. & Westendorp, R. Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65. Sci. Rep. 13, 1203 (2023).

    Article  Google Scholar 

  43. Ramola, R., Jain, S. & Radivojac, P. Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies. Pac. Symp. Biocomput. 24, 124–135 (2019).

    Google Scholar 

  44. Geifman, Y. & El-Yaniv, R. Selective classification for deep neural networks. In Proc Advances in Neural Information Processing Systems (eds Guyon, I et al.) 30 (Curran Associates, 2017).

  45. Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). Proc. ICML 30, 2668–2677 (2018).

    Google Scholar 

  46. Narayan, A., Berger, B. & Cho, H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. 39, 765–774 (2021).

    Article  Google Scholar 

  47. Atanasova, P., Simonsen, J. G., Lioma, C. & Augenstein, I. A diagnostic study of explainability techniques for text classification. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (eds Webber, B. et al.) 3256–3274 (ACL, 2020).

  48. Bastings, J. & Filippova, K. The elephant in the interpretability room: why use attention as explanation when we have saliency methods? In Proc. Third Blackbox NLP Workshop on Analyzing and Interpreting Neural Networks for NLP (eds. Alishashi A. et al.) 149–155 (ACL, 2020).

  49. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at (2021).

  50. Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The power of personality: the comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2, 313–345 (2007).

    Article  Google Scholar 

  51. Stewart, R. D., Mõttus, R., Seeboth, A., Soto, C. J. & Johnson, W. The finer details? The predictability of life outcomes from Big Five domains, facets and nuances. J. Pers. 90, 167–182 (2022).

    Article  Google Scholar 

  52. McCrae, R. R. & Costa, P. T. Jr. in Handbook of Personality: Theory and Research (eds John, O. P. & Robins, R. W.) 159–181 (Guilford Press, 2008).

  53. Zettler, I., Thielmann, I., Hilbig, B. E. & Moshagen, M. The nomological net of the HEXACO model of personality: a large-scale meta-analytic investigation. Perspect. Psychol. Sci. 15, 723–760 (2020).

    Article  Google Scholar 

  54. Det Danske Personligheds Og Sociale Adfærdspanel (accessed 21 March 2021).

  55. Gangl, M. Changing labour markets and early career outcomes: labour market entry in Europe over the past decade. Work Employ. Soc. 16, 67–90 (2002).

    Article  Google Scholar 

  56. Halleröd, B., Ekbrand, H. & Bengtsson, M. In-work poverty and labour market trajectories: poverty risks among the working population in 22 European countries. J. Eur. Public Policy 25, 473–488 (2015).

    Google Scholar 

  57. Mackenbach, J. P. et al. Socioeconomic inequalities in health in 22 European countries. N. Engl. J. Med. 358, 2468–2481 (2008).

    Article  Google Scholar 

  58. Adler, N. E. & Ostrove, J. M. Socioeconomic status and health: what we know and what we don’t. Ann. N. Y. Acad. Sci. 896, 3–15 (1999).

    Article  Google Scholar 

  59. Liao, T. F. et al. Sequence analysis: its past, present and future. Soc. Sci. Res. 107, 102772 (2022).

    Article  Google Scholar 

  60. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (European Parliament & Council of the European Union);

  61. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 115 (2021).

    Google Scholar 

  62. Burkart, N. & Huber, M. F. A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021).

    Article  MathSciNet  Google Scholar 

  63. Madiega, T. Artificial Intelligence Act (European Parliament, 2023);

  64. Eurostat. European system of accounts. ESA 2010 Publications Office of the European Union, 2013. Off. J. Eur. Un. 174, 56 (2013).

    Google Scholar 

  65. Biś, D., Podkorytov, M. & Liu, X. Too much in common: shifting of embeddings in transformer language models and its implications. In Proc. 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Toutanova, K. et al.) 5117–5130 (ACL, 2021).

  66. Beltagy, I., Peters, M. E. & Cohan, A. Longformer: the long-document transformer. Preprint at (2020).

  67. Wettig, A., Gao, T., Zhong, Z. & Chen, D. Should you mask 15% in masked language modeling? In Proc. 17th Conference of the European Chapter of the Association for Computational Linguistics (eds Vlachos, A. & Augenstein, I.) 2985–3000 (ACL, 2023).

  68. Jawahar, G., Sagot, B. & Seddah, D. What does BERT learn about the structure of language? In Proc. 57th Annual Meeting of the Association for Computational Linguistics (eds Korhonen, A. et al.) 3651–3657 (ACL, 2019).

  69. Sun, C., Qiu, X., Xu, Y. & Huang, X. How to fine-tune BERT for text classification? Proc. CCl 11856, 194–206 (2019).

    Google Scholar 

  70. Huang, S., Wang, S., Li, D. & Jiang, L. AUL is a better optimization metric in PU learning. Preprint at (2023).

  71. Wilmoth, J. R. et al. in Methods Protocol for the Human Mortality Database 10–11 (Univ. California Berkeley and Max Planck Institute for Demographic Research, 2007).

  72. Lee, K. & Ashton, M. C. Psychometric properties of the HEXACO personality inventory. Multivariate Behav. Res. 39, 329–358 (2004).

    Article  Google Scholar 

  73. Yu, S. et al. A re-balancing strategy for class-imbalanced classification based on instance difficulty. In Proc. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (ed. O'Conner L.) 70–79 (IEEE, 2022).

  74. Müller, R., Kornblith, S. & Hinton, G. E. When does label smoothing help? In Adv. Neural Information Processing Systems 32 (NeurIPS 2019) (eds H. Wallach. et al.). 32, 4694–4703 (Curran Associates, 2019).

  75. Polat, G. et al. Class distance weighted cross-entropy loss for ulcerative colitis severity estimation. Proc. MIUA 13413, 157–171 (2022).

    Google Scholar 

  76. Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. Proc. IEEE PAMI 2, 318–327 (2018).

    Google Scholar 

  77. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (ed. O'Conner L.) (CVPR) 2818–2826 (IEEE, 2016).

  78. Groenendijk, R., Karaoglu, S., Gevers, T. & Mensink, T. Multi-loss weighting with coefficient of variations. In Proc. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) 1468–1477 (IEEE, 2021).

  79. Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis-connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

    Google Scholar 

  80. Liang, Y., Cao, R., Zheng, J., Ren, J. & Gao, L. Learning to remove: towards isotropic pre-trained BERT embedding. In Proc. Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks (eds. Farkaš I. et al.) 448–459 (ACM, 2021).

  81. Mu, J., Bhat, S. & Viswanath, P. All-but-the-top: simple and effective postprocessing for word representations. Preprint at (2023).

  82. Savcisens, G. Socialcomplexitylab/life2vec. Zenodo (2023).

Download references


We thank S. M. Hartmann for help with structuring and refactoring the code and M. F. Odgaard as well as the entire Social Complexity Lab for helpful feedback and discussions. The work was funded by the Villum Foundation Grant Nation-Scale Social Networks (to S.L.).

Author information

Authors and Affiliations



S.L. and G.S. conceived and designed the analysis. G.S. implemented the computational framework and performed the analysis, and T.E.-R., L.K.H., L.H.M., L.L., A.R., I.Z. and S.L. supported the analysis. T.E.-R., A.R. and L.K.H. contributed to the algorithmic auditing. A.R. contributed to the methodology of the transformer architecture. S.L., L.K.H. and L.H.M. refined the statistical evaluations and methodology. L.L. and I.Z. contributed data and refined the methodology for the personality nuances predictions. All authors read and approved the manuscript.

Corresponding author

Correspondence to Sune Lehmann.

Ethics declarations

Competing interests

The authors declare no competing interests.

Inclusion and ethics statement

This study relies on secondary analysis of administrative data and does not require approval from the Danish committee system established under the Danish Act on Research Ethics Review of Health Research Projects. The data analysis was conducted in accordance with the rules set by the Danish Data Protection Agency and the information security and data confidentiality policies of Statistics Denmark. See Methods section Ethics and broader impacts, for further information.

Peer review

Peer review information

Nature Computational Science thanks Michal Kosinski, Denis Helic and Dashun Wang for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary sections 1–5, Figs. 1–10 and Tables 1–12.

Reporting Summary

Peer Review File

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Savcisens, G., Eliassi-Rad, T., Hansen, L.K. et al. Using sequences of life-events to predict human lives. Nat Comput Sci 4, 43–56 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics