Data
Work Package 1: Collecting Data
“Data Access, Data Wrangling, Data Engineering” AI MULTIPLY researchers will make use of multiple, large patient datasets, both national and…
Our Mission
Using the collective expertise of patients, clinicians, researchers and artificial intelligence to improve the care of people who live with many health conditions and medicines
We are a Research Collaborative which has been awarded funding from the National Institute for Health and Care Research (NIHR) to investigate the use of artificial intelligence for multiple long-term health conditions (multimorbidity) and taking multiple medications (polypharmacy).
Using AI to characterise the dynamic inter-relationships between MUltiple Long-term CondiTIons and PoLYpharmacy and across diverse UK populations and inform health care pathways (AI-MULTIPLY).
We are dedicated to advancing healthcare through innovative research and cutting-edge AI technology.
Our aim is to unravel the complexities of pharmacy and disease clustering across life-course.
To understand the interactions between polypharmacy and disease clustering over a person’s life-course.
Using artificial intelligence (AI) to characterise the dynamic inter-relationships between MUltiple Long-term conditIons (MLTCs) and Polypharmacy and across diverse UK populations and inform health care pathways (AI-MULTIPLY).
The project aims to characterise the dynamic relationships of MLTC and polypharmacy and inform healthcare pathways.
The project is based in Newcastle University and Queen Mary University, London.
Using the collective expertise of patients, clinicians, researchers and artificial intelligence to improve the care of people who live with many health conditions and medicines.
Our goal at AI MULTIPLY is to leverage the power of artificial intelligence to address some of the most pressing challenges in healthcare.
We are focused on understanding how the concurrent use of multiple medications (polypharmacy) interacts with disease clustering to affect patient outcomes.
AI MULTIPLY brings together researchers from a number of organisations including:
Barts Health NHS Trust
Bradford Teaching Hospitals NHS Foundation Trust
Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust
Newcastle University
Newcastle upon Tyne Hospitals NHS Foundation Trust
Queen Mary University London
Social Action for Health
The University of Edinburgh
We are strengthened by partnerships with leading institutions dedicated to advancing healthcare research and innovation.
Our group is made up of experts across a range of fields: from research specialists who are experts in the data science field to clinicians with years of experience treating patients. Regardless of their background, each member of the consortium is dedicated to using AI and machine learning to develop strategies for prevention and improved management of multiple long term conditions.
Work package 1 & 2
DATA ACCESS, DATA WRANGLING, DATA ENGINEERING/
AI FOR MLTC AND POLYPHARMACY CLUSTERS, TIME COURSE AND TRAJECTORIES
Professor Michael R Barnes – Work Package Lead
Professor of Bioinformatics and Director of the Centre for Translational Bioinformatics (Queen Mary’s University London)
Work package 3
TOWARDS INTERPRETATION AND CLINICAL DECISION MAKING
Dr Dexter Canoy – Work Package Lead
Senior Lecturer in Epidemiology and Public Health (Newcastle University)
Professor Barbara Hanratty – Work Package Lead
Professor of Primary Care and Public Health (Newcastle University)
Dr Soraia Sousa – Work Package Lead
Mental Health Lead. Honorary Clinical Senior Lecturer (Newcastle University)
Work package 4
SOCIAL SCIENCE INVESTIGATION OF INTERDISCIPLINARY ENTANGLEMENTS
Work package 5
HEALTH AND SOCIAL CARE OUTCOMES: TRANSLATION INTO PRACTICE
Cross-cutting
INEQUALITIES
We aim to better understand the relationships between MLTC, polypharmacy, and personal/social factors to optimise treatment for individual patients.
The project will focus on five different aspects of research, known as work packages (WPs), as well as two cross cutting themes, Inequalities and PPIE which are summarised in the sections below.
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