Search: onr:"swepub:oai:DiVA.org:liu-179195" >
Hybrid modelling fo...
Hybrid modelling for stroke care : Review and suggestions of new approaches for risk assessment and simulation of scenarios
-
- Herrgårdh, Tilda (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
-
- Madai, Vince I (author)
- Charite Univ Med Berlin, Germany; Birmingham City Univ, England
-
- Kelleher, John D. (author)
- Technol Univ Dublin, Ireland
-
show more...
-
- Magnusson, Rasmus (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
-
- Gustafsson, Mika (author)
- Linköpings universitet,Bioinformatik,Tekniska fakulteten
-
- Milani, Lili (author)
- Univ Tartu, Estonia
-
- Gennemark, Peter (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,AstraZeneca, Sweden
-
- Cedersund, Gunnar (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
-
show less...
-
(creator_code:org_t)
- Elsevier Science Ltd, 2021
- 2021
- English.
-
In: NeuroImage. - : Elsevier Science Ltd. - 2213-1582. ; 31
- Related links:
-
https://liu.diva-por... (primary) (Raw object)
-
show more...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Keyword
- Stroke; Mechanistic modelling; Machine learning; Bioinformatics; Precision medicine
Publication and Content Type
- ref (subject category)
- for (subject category)
Find in a library
-
NeuroImage
(Search for host publication in LIBRIS)
To the university's database