Imperial College London
Alternate

EUR ING Dr Edward A Meinert

Faculty of Medicine, School of Public Health

Honorary Senior Lecturer
 
 
 
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Contact

 

e.meinert14

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Milne-Ives:2021:10.2196/preprints.35738,
author = {Milne-Ives, M and Fraser, LK and Khan, A and Walker, D and van, Velthoven MH and May, J and Wolfe, I and Harding, T and Meinert, E},
doi = {10.2196/preprints.35738},
title = {Life Course Digital Twins–Intelligent Monitoring for Early and Continuous Intervention and Prevention (LifeTIME): Proposal for a Retrospective Cohort Study (Preprint)},
url = {http://dx.doi.org/10.2196/preprints.35738},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <sec>                    <title>BACKGROUND</title>                        <p>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.</p>                </sec>                                <sec>                    <title>OBJECTIVE</title>                        <p>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.</p>                </sec>                                <sec>                    <title>METHODS</title>                        <p>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.
AU  - Milne-Ives,M
AU  - Fraser,LK
AU  - Khan,A
AU  - Walker,D
AU  - van,Velthoven MH
AU  - May,J
AU  - Wolfe,I
AU  - Harding,T
AU  - Meinert,E
DO  - 10.2196/preprints.35738
PY  - 2021///
TI  - Life Course Digital Twins–Intelligent Monitoring for Early and Continuous Intervention and Prevention (LifeTIME): Proposal for a Retrospective Cohort Study (Preprint)
UR  - http://dx.doi.org/10.2196/preprints.35738
ER  -
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