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Hybrid modelling fo...
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Herrgårdh, TildaLinköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
(author)
Hybrid modelling for stroke care : Review and suggestions of new approaches for risk assessment and simulation of scenarios
- Article/chapterEnglish2021
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Elsevier Science Ltd,2021
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LIBRIS-ID:oai:DiVA.org:liu-179195
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179195URI
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https://doi.org/10.1016/j.nicl.2021.102694DOI
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:for swepub-publicationtype
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Funding Agencies|Swedish research councilSwedish Research CouncilEuropean Commission [2018-05418, 2018-03319]; CENIIT [15.09]; Swedish foundation for strategic researchSwedish Foundation for Strategic Research [ITM17-0245]; SciLifeLab/KAW national COVID-19 research program [2020.0182]; Swedish Fund for Research without Animal Experiments; ELLIIT; H2020 [777107]
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Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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Madai, Vince ICharite Univ Med Berlin, Germany; Birmingham City Univ, England
(author)
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Kelleher, John D.Technol Univ Dublin, Ireland
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Magnusson, RasmusLinköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten(Swepub:liu)rasma87
(author)
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Gustafsson, MikaLinköpings universitet,Bioinformatik,Tekniska fakulteten(Swepub:liu)mikgu75
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Milani, LiliUniv Tartu, Estonia
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Gennemark, PeterLinköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,AstraZeneca, Sweden(Swepub:liu)petge96
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Cedersund, GunnarLinköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten(Swepub:liu)gunce57
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Linköpings universitetAvdelningen för medicinsk teknik
(creator_code:org_t)
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In:NeuroImage: Elsevier Science Ltd312213-1582
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