Using sequences of life-events to predict human lives

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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.
TidsskriftNature Computational Science
Sider (fra-til)43–56
Antal sider14
StatusUdgivet - 2024

Bibliografisk note

Funding Information:
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.).

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.

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