In diabetics, hypoglycaemia usually doesn’t occur randomly, just as a stock market price doesn’t crash for no reason. That means both are also predictable, at least theoretically. In practice, however, such forecasts have so far succeeded only in the rarest of cases. But if Alexander Marx’s project is successful, that will change for children with type 1 diabetes. “We’re working on predictive models that can detect early on if there is a risk of hypoglycaemia during the night,” explains the ETH AI Center Fellow, adding: “When children engage in vigorous physical activity during the day, their blood glucose levels can drop below a critical threshold while they sleep. With a reliable forecasting model, this risk could be avoided.”
Bringing cause-and-effect networks to light
Marx is exploring this hypothesis as part of Julia Vogt’s Medical Data Science Group. “I come from more of a theoretical background and have worked mostly with artificially generated data. The AI Center’s purpose is to bring together theory and practice, which I find exciting. I now have to make my theoretical concepts work with real data.”
Marx acquired his academic credentials at Saarland
in Saarbrücken, Germany. After completing a Master’s degree in bioinformatics, he stayed on there to write his doctoral thesis at the Max Planck Institute for Informatics. His thesis examined causal discovery – statistical methods that can be used to create causal graphs from observational data, which make cause-and-effect networks visible.
Deriving predictions from correlations
One way to apply these methods is to use survey data to identify all factors that are suspected of having an effect on a particular variable. A general example would be how a person’s income depends on their age, place of residence, gender, education, marital status or number of children. Based on the correlations found, predictions can then be made for individuals who were not surveyed. Marx clarifies that to do this, it’s not even necessary to define the entire dependency chains; it’s enough to elicit the smallest set of factors required to make a prediction.
From synthetic data to clinical reality
With the help of artificial intelligence based on simulated data, Marx used these methods to study how the activities of about 500 selected genes in a human cell are related. Ideally, these methods can be scaled up in the future to include all of a cell’s 25,000 or so genes. Such computer analyses of gene networks would easily and quickly provide biological and medical research with a comprehensive understanding of the processes that take place in a cell. Achieving this through laboratory experiments would require enormous effort, as the scientists would have to switch off each gene individually using genetic engineering tools and then measure how this affects the activity of all the other genes.