By Peter Aagaard Brixen
In a new scientific article, ‘Using sequences of life-events to predict human lives’, published in Nature Computational Science, researchers have analyzed health data and attachment to the labor market for 6 million Danes in a model dubbed life2vec. After the model has been trained in an initial phase, i.e., learned the patterns in the data, it has been shown to outperform other advanced neural networks and predict outcomes such as personality and time of death with high accuracy (https://www.nature.com/articles/s43588-023-00573-5).
The predictions from Life2vec are answers to general questions such as: ‘death within four years’? When the researchers analyze the model’s responses, the results are consistent with existing findings within the social sciences; for example, all things being equal, individuals in a leadership position or with a high income are more likely to survive, while being male, skilled or having a mental diagnosis is associated with a higher risk of dying. Life2vec encodes the data in a large system of vectors, a mathematical structure that organizes the different data. The model decides where to place data on the time of birth, schooling, education, salary, housing and health.
The researchers behind the article point out that ethical questions surround the life2vec model, such as protecting sensitive data, privacy, and the role of bias in data. These challenges must be understood more deeply before the model can be used, for example, to assess an individual’s risk of contracting a disease or other preventable life events.
According to the researchers, the next step would be to incorporate other types of information, such as text and images or information about our social connections. This use of data opens up a whole new interaction between social and health sciences.
A transformer model is a type of AI, deep learning data architecture used to learn about language and other tasks. The models can be trained to understand and generate language. The transformer model is designed to be faster and more efficient than previous models and is often used to train large language models on large datasets.
A neural network is a computer model inspired by the brain and nervous system of humans and animals. There are many different types of neural networks (e.g. transformer models).
Like the brain, a neural network is made up of (artificial) neurons. These neurons are connected and can send signals to each other. Each neuron receives input from other neurons and then calculates an output that is passed on to other neurons.
A neural network can learn to solve tasks by training on large amounts of data.
Neural networks rely on training data to learn and improve their accuracy over time. But once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence that, according to researchers, allow us to classify and group data at high speed. One of the most well-known neural networks is Google’s search algorithm.