dc.contributor.author | Kelleher, John | |
dc.date.accessioned | 2022-03-21T13:30:30Z | |
dc.date.available | 2022-03-21T13:30:30Z | |
dc.date.issued | 2021 | |
dc.date.submitted | 2021 | en |
dc.identifier.citation | Herrgårdh, T. and Madai, V.I. and Kelleher, J.D. and Magnusson, R. and Gustafsson, M. and Milani, L. and Gennemark, P. and Cedersund, G., Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios, NeuroImage: Clinical, 2021, 31, 102694 | en |
dc.identifier.other | Y | |
dc.identifier.uri | http://hdl.handle.net/2262/98331 | |
dc.description.abstract | 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 imple-
mented 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 | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | NeuroImage: Clinical; | |
dc.relation.ispartofseries | 31; | |
dc.relation.ispartofseries | 102694; | |
dc.rights | Y | en |
dc.subject | Stroke | en |
dc.subject | hybrid modelling | en |
dc.subject | Precision Medicine for stroke | en |
dc.subject | Precision medicine | en |
dc.subject | Bioinformatics | en |
dc.subject | Machine learning | en |
dc.subject | Mechanistic modelling | en |
dc.title | Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/kellehjd | |
dc.identifier.rssinternalid | 239685 | |
dc.identifier.doi | http://dx.doi.org/10.1016/j.nicl.2021.102694 | |
dc.rights.ecaccessrights | openAccess | |