Artificial Intelligence for Multiple Long-Term Conditions (AIM) Engagement Workshop 2 17 th July 2020, 10am-12pm Where in the world are you today? Pin your location to the map! (link in the chat) https://padlet.com/aimcallmltcs/8uydjkku6hpbjy5r
Welcome and Agenda: 1000 Welcome, introduction to the call and reflections on the first engagement event 1010 Introduction to the NIHR Research Design Service (RDS) 1020 Keynote speakers: ● Professor Ben MacArthur, Deputy Programme Director for Health and Medical Sciences, Alan Turing Institute ● Professor Tom Marshall, University of Birmingham, NIHR-MRC 2018 Understanding multimorbidity in the UK call ● Joyce Fox and Kate Ripley, Experts by experience 1045 Breakout session 1: 60 second pitches and discussion (Zoom) 1115 Coffee Break 1125 Breakout session 2: Choose your breakout session (MS Teams) ● Patient and public involvement ● Learning from failure ● NIHR Research Design Service ● Data 1145 Application process, future events and Q&A
Padlet: Locations of Participants
Overview: Use AI and data science methods, combined with existing methodology and expertise in clinical practice, applied health and care research and social science, to systematically identify, map or explore clusters of disease Seek research to better understand the trajectories of patients with MLTC-M over time and throughout the life course, including the influence of wider determinants such as environmental, behavioural and psychosocial factors To fund multidisciplinary Research Collaborations to undertake programmes of work: • Development Awards of up to £120k over 8 months • Research Collaborations of £2.5-5m over 36 months (wave 1) or 30 months (wave 2) Establish a Research Support Facility (RSF) (£3m) to support successful applicants.
Keynote Speakers: • Ben MacArthur: Professor in Mathematics at the Life Science Interface, University of Southampton and Deputy Programme Director for Health and Medical Sciences, Alan Turing Institute. • Tom Marshall: Professor of Public Health and Primary Care, University of Birmingham. Funding award holder for NIHR-MRC project - Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM) • Joyce Fox and Kate Ripley: Experts by experience and members of the Economics of Health and Social Care Interface Policy Research Unit PPI Group.
NIHR AIM Call RDS Support & Advice Jörg Huber (RDS SE), Jane Fearnside (YH) & Bernadette Egan (SE) www.rds-se.nihr.ac.uk Twitter: @NIHR_RDS 17 th July 2020
About RDS • FREE confidential support for health and social care researchers across England on all aspects and methods of research design and grant application development • Expert RDS advisers can help with all aspects of designing a proposal incl.: • research design and methods • funding sources • refining research question • outcome measures • PPI and building a team • avoiding common pitfalls
We offer support along the way to ‘pressing the button’
Key elements central to NIHR applications • Project • People/team • Places • Public involvement (PPI) Priorities for NIHR : • Diversity • Inequalities and disadvantage Read and follow the guidance . If and when interacting with us, be prepared for us not to agree with you.
Resources • https://www.nihr.ac.uk/documents/research-on-multiple-long- term-conditions-multimorbidity-mltc-m/24639 • https://acmedsci.ac.uk/file-download/99630838 • https://bjgp.org/content/68/669/e245 • Kastner M, Hayden L, Wong G, et al. Underlying mechanisms of complex interventions addressing the care of older adults with multimorbidity: a realist review. BMJ Open 2019;9:e025009. doi:10.1136/bmjopen-2018-025009 Contact your RDS • https://www.nihr.ac.uk/explore-nihr/support/research-design- service.htm • https://twitter.com/nihr_rds?lang=en
Thank you Join RDS Teams Meeting later on +44 20 3443 8728 United Kingdom, London (Toll) Conference ID: 402 821 178# See Agenda for today
Finding expert partners • On multi-morbidity • Geriatrician • Generalist physicians • Core problem is shortage of doctors • RDS can help with this through putting out calls across the regions Contact your RDS • https://www.nihr.ac.uk/explore-nihr/support/research- design-service.htm
Health and Medical Sciences Prof. Ben MacArthur
NIHR AIM call: Opportunities for data science - This multi-disciplinary / cross-disciplinary / cross-institutional call offers tremendous opportunity to develop new ways of approaching multimorbidity. - New way of conducting research that develops a holistic view of life course health that combines physiological, psychosocial and environmental factors, and learns from heterogeneous linked data. - Better understanding of disease clusters and trajectories will be needed to develop integrated treatment approaches to meet the needs of people with MLTC-M. - This is hugely challenging and will require new ways of working that have not been developed yet (including ethics and data security).
NIHR AIM call: Challenges for data science - To meet these challenges requires new analysis tools that can handle complex, distributed data. - Innovation is needed in areas such as: model interpretability, causal inference, missing data, combining mechanistic and statistical models, machine learning, uncertainty quantification … - Bring together mathematicians statisticians, computer scientists toward solving this grand challenge. - This challenge = a great opportunity to think creatively about new ways of doing multi-disciplinary data science.
NIHR AIM call: Summary - Vital that these approaches are developed in close collaboration with clinical, health care research expertise and have clear benefits to patients. - Requires careful consideration of how analysis will reduce health inequalities. - Vital that we collectively develop a coordinated approach: call will stimulate collaboration between groups, assisted by a central facility. - See NIHR Strategic Framework for MLTC-M Research and the cross funder AAS, MRC, NIHR, Wellcome multimorbidity research framework.
Bringing Innovative Research Methods to Cluster Analysis of Multimorbidity (BIRMCAM) Tom Marshall 1
Collaboration • University of Birmingham • Institute of Applied Health Research • University of Cambridge • MRC Biostatistics Unit • Department of Public Health and Primary Care 2
Aims • Develop understanding of multimorbidity clustering methods • Objectives: • Critical review • Create a “methodological commons” of multimorbidity clustering methods • Apply new techniques: probabilistic machine learning (ML); multistate models that predict transition / trajectory • Implement and validate findings in two large primary care databases 3
So far • Identified 5 main approaches to multimorbidity clustering • Latent class analysis (LCA) • Hierarchical cluster analysis (HCA) • Multiple Correspondence Analysis followed by k-means (MCA-k) • k-modes (kmodes) • For binary data • k-means (kmeans) • Uses Euclidean distance & generally used for continuous variables 4
Simulated datasets • Clustering patients (not diseases) • Use Rand index to compare method to known number of clusters 5
Simulated dataset characteristics 1. Prevalence • Frequency of underlying condition 2. Noise • Random error 3. Number of clusters 4. Frequency of ‘nulls’ • Number of individuals not in any clusters 5. Correlation • Between diseases within a cluster 6. Overlap • Diseases can belong in multiple patient clusters 7. Balance • Vary number of patients in each cluster 6
Broad findings • Rand index declines with • More noise • Lower prevalence • Some methods tend to perform better • LCA (Latent Class Analysis) • HCA (Hierarchical cluster analysis) 7
Primary Care Records Analysis • Simplified primary care dataset • Real patients • Limited number of morbidities 8
Characteristics of dataset 9
Methods • THIN (IMRD) dataset • age 65- 84y, n=6,387 patients with ≥1 condition • excluding conditions with prevalence <2% • Bootstrap 500 samples from original dataset • Pre-specify the number of classes (clusters) • Run analyses • Summarise, for most frequently occurring clusters i. Proportion of dataset in cluster (median %, IQR) ii. Frequency of occurrence of each cluster (as % of 500 bootstrap samples) iii. Three most frequently occurring conditions in cluster (median %, IQR) 10
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