Clustering Multiple Long-Term Conditions

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Background
What is MLTC? Why cluster? Classification versus clustering Co-occurrence versus sequences
Using EHR data
Data sources MLTC code lists When is a disease chronic? Code frequency in EHRs Other code lists
Clustering diseases
Overview of approach Disease embedding methods Disease clustering methods Disease cluster assignments Evaluation strategy
Clustering people
Overview of approach Representation methods EHR-BERT Work in progress
Publications

Publications

Thesis

A link to my thesis will appear here once open access.

Journal articles

A full list of publications from this research project can be found below:

  1. Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Barahona M, Aylin P. Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency . BMJ Open (2023) Sep 1;13(9):e072884.
  2. Beaney T, Clarke J, Woodcock T, Majeed A, Barahona M, Aylin P. Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study . BMJ Medicine (2024); 3:e000474.
  3. Beaney T. Is consensus attainable on the definition of multiple long term conditions? BMJ 384, q230 (2024).
  4. Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Barahona M, Aylin P. Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions . Journal of Multimorbidity and Comorbidity 14, 26335565241247430 (2024).
  5. Beaney T, Jha S, Alaa A, Smith A, Clarke J, Woodcock T, Majeed A, Aylin P, Barahona M. Comparing natural language processing representations of coded disease sequences for prediction in electronic health records . J Am Med Inform Assoc ocae091 (2024).
  6. Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Aylin P, Barahona M. Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England . Commun Med (Lond) 4, 102 (2024).

Conference presentations

  1. Beaney T, Jha S, Alaa A, Smith A, Clarke J, Woodcock T, Majeed A, Aylin P, Barahona M. Learning patient representations of coded disease sequences for prediction in primary care electronic health records. Machine Learning for Healthcare 2024, August 2024, Toronto, Canada.
  2. Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Aylin P, Barahona M. Identifying multi-resolution clusters of diseases in people with multimorbidity using natural language processing and graph-based clustering. NetSciX conference 2024, January 2024, Venice, Italy.
  3. Beaney T, Clarke J, Woodcock T, Majeed A, Barahona M, Aylin P. Measuring multimorbidity prevalence: impact and inequalities of using different timescales to define long-term conditions. Royal College of General Practitioners Annual Conference 2023, October 2023, Glasgow, United Kingdom.