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Hybrid modelling fo...
Hybrid modelling for stroke care : Review and suggestions of new approaches for risk assessment and simulation of scenarios
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- Herrgårdh, Tilda (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
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- Madai, Vince I (författare)
- Charite Univ Med Berlin, Germany; Birmingham City Univ, England
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- Kelleher, John D. (författare)
- Technol Univ Dublin, Ireland
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- Magnusson, Rasmus (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
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- Gustafsson, Mika (författare)
- Linköpings universitet,Bioinformatik,Tekniska fakulteten
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- Milani, Lili (författare)
- Univ Tartu, Estonia
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- Gennemark, Peter (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,AstraZeneca, Sweden
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- Cedersund, Gunnar (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
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(creator_code:org_t)
- Elsevier Science Ltd, 2021
- 2021
- Engelska.
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Ingår i: NeuroImage. - : Elsevier Science Ltd. - 2213-1582. ; 31
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Nyckelord
- Stroke; Mechanistic modelling; Machine learning; Bioinformatics; Precision medicine
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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