Aims: Our research aims to better understand how multiple long-term health conditions develop and interact over time, identify the causes of these conditions and assess the disease burden due to multimorbidity. By using advanced data analysis methods, we want to identify distinct groups of individuals with similar health profiles. This will help us tailor healthcare interventions to meet the specific needs of these groups and ultimately improve patient outcomes.
Scientific Rationale: To achieve these aims, we want to leverage UK Biobank data to access to large, annotated image and omics data. The underlying assumption is that these images and omics data contain biological information, which can help to better assess the metabolic state of a person or to predict the progression of multimorbidity. We’ll be using cutting-edge techniques like causal inference, machine learning, and clustering analysis to analyze large datasets of patient health records. By doing this, we hope to uncover hidden patterns in multimorbidity progression and identify factors that contribute to different health profiles.
Project Duration: The research project is expected to run for 5 years.
Public Health Impact: This research has the potential to inform better healthcare policies and practices by identifying underlying causes of multimorbidity. By understanding how different groups of people experience multimorbidity and its causes, we can work towards more personalized approaches to healthcare. Ultimately, this could lead to improved public health outcomes and more effective use of healthcare resources.