Artificial Intelligence for Multiple Long-term conditions (AIM): A consensus statement from the NIHR AIM consortia

Abstract

Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When combined with expertise in clinical practice, applied health research and social science, there is potential to systematically identify and map new clusters of disease, understand the trajectories of patients with these conditions throughout their life course, predict serious adverse outcomes, optimise therapies and consider the influence of wider determinants such as environmental, behavioural and psychosocial factors. The National Institute of Health Research (NIHR) recently funded multidisciplinary consortia to bring together AI specialists, experts in big data and MLTC-M in the first and second waves of this new programme. The so-called AIM consortia of researchers will spearhead the use of artificial intelligence methods and develop insights for the identification and subsequent prevention of MLTC-M. This consensus agreement is aimed at facilitating a community of learning within the AIM consortia, promoting cooperation, transparency and rigour in our approaches while maintaining high methodological standards and consistency in defining and reporting within our research. In bringing together these research collaborations, there is also an opportunity to foster shared learning, synergies and rapidly compare and validate new AI approaches across our respective studies. This step is critical to implementation on the pathway to patient and public benefit. Scope and aimThis statement was developed by the first wave of the NIHR AIM consortia and received input from the second wave. It includes representatives across thirteen universities from Edinburgh,

Cohort profile for the creation of the SAIL MELD-B e-cohort (SMC) and SAIL MELD-B children and Young adult e-cohort (SMYC)

“…The MELD-B clinical domain expert group proposed an initial set of long-term conditions based on the list agreed upon by the NIHR AIM Research Consortia [55], existing literature and project requirements [56][57][58][59].…”

Asking questions that are “close to the bone”: integrating thematic analysis and natural language processing to explore the experiences of people with traumatic brain injuries engaging with patient-reported outcome measures

“…As for any other research paradigm, one of the main goals of mixed-methods research integrating NLP methods will be to abide by high methodological standards, minimising the risk of bias and maximising the accuracy and credibility of research results ( 111 ). To do so, researchers will need to strive for rigour by adopting a coherent application of analytical methods and clear standards of honesty and transparency in reporting the study results ( 112 , 113 ).…”