HDR UK Longitudinal Data Modelling Symposium (03:47:34)

Monday 31st March 13.00-17.00

Hosted by HDR UK’s Big Data for Complex Disease consortium, this symposium brought together researchers from across HDR UK’s Driver Programmes and wider Institute who are interested in using health data modelling to understand risk and disease trajectories, better-informing prediction, prevention, and treatment across the population. The symposium was an exciting opportunity for researchers working with novel methodologies and tackling challenges in working with health data across various diseases, co-morbidities, and types of EHRs to share their work and insights.

Keynotes

The symposium featured two excellent keynote speakers who discussed statistical and machine learning approaches to modelling longitudinal data:

Professor Tingting Zhu, Associate Professor in AI for Digital Health in the Department of Engineering Science at Oxford. Her research interests lie in machine learning for healthcare applications, and she has developed probabilistic techniques for reasoning about time-series medical data. Her work involves the development of machine learning for understanding complex patient data, with an emphasis on Bayesian inference, deep learning, and applications involving the developing world.

Professor George Ploudidis, Professor of Population Health and Statistics at the UCL Social Research Institute. He is a multidisciplinary quantitative social scientist and a longitudinal survey methodologist. His research interests relate to socioeconomic and demographic determinants of health over the life course and the mechanisms that underlie generational differences in health, well-being and mortality. His methodological work in longitudinal surveys focuses on applications for handling missing data, causal inference and measurement error.

Short Talks

Read and reviewed by public contributors and symposium organisers, abstracts for six Short Talks were chosen and featured as part of the symposium:

• Longitudinal Analysis of Disease Trajectories of Multimorbidity through Process Mining Techniques – Areti Manataki, University of St Andrew
• Longitudinal Study of Neighbourhood Characteristics and Mental Health in Childhood and Adolescence in England – Niloofar Shoari, Great Ormond Street Institute of Child Health, University College London
• Characterising post-myocardial infarction disease trajectories using non-negative matrix factorisation: a nationwide analysis – Jonathan A. Batty, Leeds Institute for Data Analytics, University of Leeds
• Use of national data collections to understand Lynch syndrome diagnostic testing pathways in endometrial cancers – Catherine Huntley, Institute of Cancer Research
• Inequalities in asthma outcomes in England: a national cohort study – Zakariah Gassasse, Imperial College London
• Mortality in adolescents with and without neurodisability in England: a national cohort study using linked health and education data from ECHILD – Louise Macaulay, University College London 

Additional Resources:

• Predicting Type 2 Diabetes in Individuals with Severe Mental Illness (SMI)

• An assessment of trajectory analysis for disease risk prediction: a scoping review

• Validation of PREdiction of DELIRium in ICu patients (PRE-DELIRIC) model for ICU delirium in general ICU and patients with liver disease: A retrospective cohort study

• Trends in the assessment, management, and outcomes of patients with suspected acute coronary syndrome using the DataLoch Heart Disease Registry