  Over the past decade, machine learning has revolutionized computers' ability
to analyze text through flexible computational models. Due to their structural
similarity to written language, transformer-based architectures have also shown
promise as tools to make sense of a range of multi-variate sequences from
protein-structures, music, electronic health records to weather-forecasts. We
can also represent human lives in a way that shares this structural similarity
to language. From one perspective, lives are simply sequences of events: People
are born, visit the pediatrician, start school, move to a new location, get
married, and so on. Here, we exploit this similarity to adapt innovations from
natural language processing to examine the evolution and predictability of
human lives based on detailed event sequences. We do this by drawing on
arguably the most comprehensive registry data in existence, available for an
entire nation of more than six million individuals across decades. Our data
include 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
identify new potential mechanisms that impact life outcomes and associated
possibilities for personalized interventions.
