Theme: Lung / Respiratory
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Project reference: DL_2023_032

Project Lead: Holly Tibble

Lung conditions like asthma and COPD can be incredibly unpredictable, and it can be very hard to foretell when an attack is likely to occur. Inconsistent use of an inhaler (or other treatment), smoking, obesity, history of respiratory infections, and more, are associated with higher risk of attacks.  Despite knowing so many risk factors, identifying who is actually going to have an attack has proven a challenging task.  

Our risk prediction uses machine learning algorithms, applied to routine health data, like a GPs record of a consultation, to estimate asthma attacks.  Machine learning models are able to account for interactions – specific combinations of risk factors which are treated as more than the sum of their parts.  These methods often estimate outcomes very accurately, but they require a lot of data and a lot of computing power.  This makes them highly suitable for analysis of routine data, which is collected every day on a large scale.  

In this project, we aim to test a previously developed machine learning model in a new population, and ensure that it works well for all patients, including young people and people with multiple health conditions.

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