The trajectories of complex disease
The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnosis and enable personalised medicine, motivating efforts to identify disease commonalities and subtypes from patient comorbidity information and other modalities. We have developed an age-dependent topic-modelling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets and applied it to c. 300,000 individuals from UK Biobank and >200,000 individuals from the All of Us program. A surprisingly small number of disease trajectories capture known and novel combinations of disorders that occur throughout life and identify disease subtypes that occur in multiple topics, with differential genetic risk profiles. Such stratification improves understanding of patient risk and heterogeneity, leading to better identification of genetic risk, characterisation of pathological pathways and the discovery of new therapeutic targets.
Date: 24 October 2024, 15:30
Venue: 24-29 St Giles', 24-29 St Giles' OX1 3LB
Venue Details: Large Lecture Theatre, Department of Statistics
Speaker: Professor Gil McVean (The Ellison Institute of Technology, Oxford)
Organising department: Department of Statistics
Organisers: Beverley Lane (Department of Statistics, University of Oxford), Professor Simon Myers (University of Oxford)
Organiser contact email address: events@stats.ox.ac.uk
Hosts: Professor Christl Donnelly (University of Oxford), Professor Simon Myers (University of Oxford)
Part of: Annual: The David Blackwell Lectures
Booking required?: Recommended
Booking url: https://www.stats.ox.ac.uk/events/david-blackwell-lecture-2024
Booking email: events@stats.ox.ac.uk
Cost: No charge
Audience: Members of the University only
Editor: Beverley Lane