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dc.contributor.authorMilne-Ives, Madison
dc.contributor.authorFraser, L
dc.contributor.authorKhan, Asiya
dc.contributor.authorWalker, David
dc.contributor.authorvan Velthoven, MH
dc.contributor.authorMay, Jon
dc.contributor.authorWolfe, I
dc.contributor.authorHarding, T
dc.contributor.authorMeinert, Edward
dc.date.accessioned2022-03-10T07:36:03Z
dc.date.issued2022-05-26
dc.identifier.issn1929-0748
dc.identifier.issn1929-0748
dc.identifier.urihttp://hdl.handle.net/10026.1/18926
dc.descriptionFile replaced incorrect version) on 8/6/22 by KT (LDS).
dc.description.abstract

Background Multimorbidity, which is associated with significant negative outcomes for individuals and health care systems, is increasing in the United Kingdom. However, there is a lack of knowledge about the risk factors (including health, behavior, and environment) for multimorbidity over time. An interdisciplinary approach is essential, as data science, artificial intelligence, and engineering concepts (digital twins) can identify key risk factors throughout the life course, potentially enabling personalized simulation of life-course risk for the development of multimorbidity. Predicting the risk of developing clusters of health conditions before they occur would add clinical value by enabling targeted early preventive interventions, advancing personalized care to improve outcomes, and reducing the burden on health care systems. Objective This study aims to identify key risk factors that predict multimorbidity throughout the life course by developing an intelligent agent using digital twins so that early interventions can be delivered to improve health outcomes. The objectives of this study are to identify key predictors of lifetime risk of multimorbidity, create a series of simulated computational digital twins that predict risk levels for specific clusters of factors, and test the feasibility of the system. Methods This study will use machine learning to develop digital twins by identifying key risk factors throughout the life course that predict the risk of later multimorbidity. The first stage of the development will be the training of a base predictive model. Data from the National Child Development Study, the North West London Integrated Care Record, the Clinical Practice Research Datalink, and Cerner’s Real World Data will be split into subsets for training and validation, which will be done following the k-fold cross-validation procedure and assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). In addition, 2 data sets—the Early-Life Data Cross-linkage in Research study and the Children and Young People’s Health Partnership randomized controlled trial—will be used to develop a series of digital twin personas that simulate clusters of factors to predict different risk levels of developing multimorbidity. Results The expected results are a validated model, a series of digital twin personas, and a proof-of-concept assessment. Conclusions Digital twins could provide an individualized early warning system that predicts the risk of future health conditions and recommends the most effective intervention to minimize that risk. These insights could significantly improve an individual’s quality of life and healthy life expectancy and reduce population-level health burdens. International Registered Report Identifier (IRRID) PRR1-10.2196/35738

dc.format.extente35738-e35738
dc.format.mediumElectronic
dc.languageen
dc.language.isoen
dc.publisherJMIR Publications Inc.
dc.subjectAI
dc.subjectNCDS
dc.subjectartificial intelligence
dc.subjecthealth care
dc.subjectmachine learning
dc.subjectmental health
dc.subjectmulitmorbidity
dc.subjectnational child development study
dc.subjectoutcome
dc.titleLife.course digital T.wins – I.ntelligent M.onitoring for E.arly and continuous intervention and prevention (LifeTIME): Proposal for a proof-of-concept study
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/35617022
plymouth.issue5
plymouth.volume11
plymouth.publication-statusPublished online
plymouth.journalJMIR Research Protocols
dc.identifier.doi10.2196/35738
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeCanada
dcterms.dateAccepted2022-03-07
dc.rights.embargodate2022-6-9
dc.identifier.eissn1929-0748
rioxxterms.versionofrecord10.2196/35738
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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