We will use computational techniques to better understand how different factors, including genetics, might affect the risk of developing severe liver diseases. This could help doctors identify and treat patients at risk more effectively.
Our goal is to create a model using machine learning techniques that can help predict the likelihood of MASLD and its severe form, MASH developing in individuals. We want to combine different types of information, like details about patients age, gender, results from medical tests, and genetic information.
We know that not everyone progresses to the severe form of these conditions at the same rate, so we want to understand why that happens. We’re particularly interested in looking at specific variations in people’s genetic makeup, called SNPs, that might be linked to a greater risk of developing severe liver scarring (fibrosis) and speeding up the progression to the severe form of the disease (MASH). By studying these genetic variations and combining them with other patient data, we hope to better identify individuals who might need closer monitoring or early intervention to prevent these serious liver conditions from developing.