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Approved Research

Advancing understanding of multi-morbidity in metabolic disease through innovation in statistical machine learning

Principal Investigator: Professor Niels Peek
Approved Research ID: 83855
Approval date: September 14th 2022

Lay summary

Many people have more than one long-term disease (known as multimorbidity). For instance, there are many people who have both heart disease and diabetes. There exist computer tools that help doctors to predict the risk that a patient will develop diseases in the future (for instance, heart disease). However, these tools always focus on single diseases. This is not helpful when we try to prevent and treat multimorbidity. In this project we will develop tools that predict the risks that a patient will develop multiple diseases (and what those diseases are).

Working out how best to treat patients with multimorbidity is not straightforward. Typically, each disease comes with its own course of treatment, but it is not clear how to combine these. Therefore, we will also develop tools that predict what would happen if a patient were given a particular treatment. Such a treatment could involve changing their lifestyle (e.g. stop smoking), taking a particular drug, or a combination of these things.

People with multimorbidity often end up taking many drugs (known as polypharmacy). This can be a problem because some drugs do not work when they are taken with other drugs. We will therefore also extend the computer tools such that they can predict what would happen if a patient, who is living with multimorbidity and is taking multiple drugs, would change their lifestyle or change the drugs that they are taking.

We will focus on patients with diabetes, heart disease, and related diseases.