Copenhagen: A new artificial intelligence system that treats human lives like language may be able to competently guess whether you’ll die within a certain period, among other life details, according to a recent study in Nature Computational Science.
The study team developed a machine-learning model called life2vec that can make general predictions about the details and course of people’s life, such as forecasts related to death, international moves and personality traits. The model draws from data on millions of residents of Denmark, including details about birth dates, sex, employment, location and use of the country’s universal healthcare system. The study metrics found the new model to be more than 78 percent accurate at predicting mortality in the research population over a four-year period, and it significantly outperformed other predictive methods such as an actuarial table and various machine-learning tools.
In a separate test, life2vec also predicted whether people would move out of Denmark over the same period with about 73 percent accuracy, per one study metric. The researchers further used life2vec to predict people’s self-reported responses to a personality questionnaire, and they found promising early signs that the model could connect personality traits with life events.
The study demonstrates an exciting new approach to predicting and analysing the trajectory of people’s life, says Matthew Salganik, a professor of sociology at Princeton University, who researches computational social science and authored the book ‘Bit by Bit: Social Research in the Digital Age’. The life2vec developers “use a very different style that, as far as I know, no one has used before,” he says.
To get a language processing tool to make predictions about people’s future, Lehmann and his colleagues processed individuals’ data into unique timelines that were composed of events such as salary changes and hospitalisations —with specific events represented as digital “tokens” that the computer could recognise. Because their training data captures so much about people and their model architecture is so flexible, the researchers suggest life2vec could offer a foundation that could be easily tweaked and fine-tuned to offer predictions about many still-unexplored aspects of a human life.
Lehmann says medical professionals have already contacted him to ask for help in developing health-related versions of life2vec—including one that could help illuminate population-level risk factors for rare diseases, for example. He hopes to use the tool to detect previously unknown relationships between the world and human life outcomes, potentially exploring questions such as “How do your relationships impact your quality of life?” and “What are the most important factors in determining salary or early death?” The tool could also tease out hidden societal biases, such as unexpected links between a person’s professional advancement and their age or country of origin.