In this section
Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.
@article{Beaney:2026,
author = {Beaney, T and Clarke, J and Woodcock, T and Majeed, A and Barahona, M and Aylin, P},
journal = {Communications Medicine},
title = {Challenges of clustering disease trajectories in people with Multiple Long-Term Conditions using data from 7.2 million patients},
year = {2026}
}
TY - JOUR
AB - BackgroundGenerating clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help target healthcare services for specific patient groups. We aimed to generate data-driven clusters of patients based on their disease trajectories and assess clinical interpretability and associations with future health outcomes.MethodsWe used structured general practice data from the Clinical Practice Research Datalink Aurum, linked to Hospital Episode Statistics data on emergency department (ED) attendances and hospital admissions, including all adults registered on 1st January 2015. For each patient, we generated a vector embedding representing their trajectory of diseases developed over time, using a transformer model, followed by clustering using k-means. Patients were subsequently followed up for 1 year to estimate associations of cluster membership with ED attendance, hospitalisation and mortality, compared to patients with no long-term conditions. We also evaluated the use of the clusters for predicting these outcomes compared with using number of long-term conditions, individual diseases or embeddings alone.Results Analysis included 5,981,091 (82.1%) patients with MLTC and 1,304,119 (17.9%) with no chronic conditions. We identified eight clusters of patients with MLTC as optimal. The largest, representing 21.3% of the population included strong contributions from cardio-kidney-metabolic conditions, but there was substantial overlap in prevalent conditions across clusters. Although large differences were found between clusters in 1-year odds of ED attendance, hospitalisation and mortality, with the highest odds in cardio-kidney-metabolic clusters, clusters explained only between 1.2-3.1% of total variance, and associations were substantially attenuated after adjustment for age, sex, ethnicity and deprivation. Clusters performed substantially worse at predicting outcomes than using the individual diseases or embeddings, and performed worse than a pe
AU - Beaney,T
AU - Clarke,J
AU - Woodcock,T
AU - Majeed,A
AU - Barahona,M
AU - Aylin,P
PY - 2026///
SN - 2730-664X
TI - Challenges of clustering disease trajectories in people with Multiple Long-Term Conditions using data from 7.2 million patients
T2 - Communications Medicine
ER -
If you are interested in meeting with members of the group please contact Marina Evangelou