HDR UK Site
We are looking to identify and characterise multimorbid phenotypes with distinct trajectories from EHR data. Implementation of machine learning algorithms to discover multimorbidity patterns (i.e. how diseases accumulate over time) and approaches to provide more insights into disease co-occurrences for supporting clinicians in taking preventive clinical actions (e.g. early disease diagnosis) for patients at risk.
- Patient and public involvement- Research presentation and discussion, Feb 2021
- Implementation of the developed approach with CALIBER dataset, Feb 2021 – Jun 2021
- Generating synthetic data sets to demonstrate the utility of the algorithm in idealised conditions, Mar 2021 – Jul 2021
- Algorithm comparison and implementation with sites, Jul 2021 – Sep 2021
- Paper write-up, May 2021 – Sep 2021
- Submitted and work in progress publications are as follows:
- Discovering multimorbidity patterns in primary care with a novel temporal phenotyping approach under uncertainty: A retrospective cohort analysis in UK
Abstract submitted to Healthcare Engineering at UCL ECR Symposium 2021, Sep 2021
- Data frameworkfor measuring multi-morbidity across different locations: the challenge for the Health Data Research (HDR) UK MM Implementation Project
Project Team and Collaborators
- Daniel Alexander
- Spiros Denaxas
- Eda Ozyigit
- Arturo Gonzalez-Izquierdo
- Muhammad Qummer Ul Arfeen
Health Informatics, Multimorbidity, Electronic Health Records, Disease Trajectories
Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach
21 June 2022
People with severe mental illness have a lower life expectancy and a higher risk of physical conditions. To improve how these comorbidities can be detected and predicted, researchers have used...
Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register
21 April 2022
People with severe mental illness, such as schizophrenia spectrum disorders or bipolar disorders, have higher death rates. It is difficult to study mental health records at scale, so a team of...
Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating unique property reference numbers to general practitioner-recorded patient addresses
20 April 2022
Being able to link addresses across systems offers a valuable resource for health data science. However, they are often not standardised despite a government push towards this. Researchers at the...