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001008816 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-02488
001008816 037__ $$aFZJ-2023-02488
001008816 041__ $$aEnglish
001008816 1001_ $$0P:(DE-Juel1)178934$$aBarakat, Chadi$$b0$$eCorresponding author$$ufzj
001008816 245__ $$aDesign and Evaluation of Parallel and Scalable Machine Learning Research in Biomedical Modelling Applications$$f2019-08-01 - 2023-06-19
001008816 260__ $$aReykjavík, Iceland$$c2023
001008816 300__ $$axxiv, 108
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001008816 502__ $$aDissertation, Háskóli Íslands, 2023$$bDissertation$$cHáskóli Íslands$$d2023$$o2023-06-19
001008816 520__ $$aThe use of Machine Learning (ML) techniques in the medical field is not a new occurrence and several papers describing research in that direction have been published. This research has helped in analysing medical images, creating responsive cardiovascular models, and predicting outcomes for medical conditions among many other applications. This Ph.D. aims to apply such ML techniques for the analysis of Acute Respiratory Distress Syndrome (ARDS) which is a severe condition that affects around 1 in 10.000 patients worldwide every year with life-threatening consequences. We employ previously developed mechanistic modelling approaches such as the “Nottingham Physiological Simulator,” through which better understanding of ARDS progression can be gleaned, and take advantage of the growing volume of medical datasets available for research (i.e., “big data”) and the advances in ML to develop, train, and optimise the modelling approaches. Additionally, the onset of the COVID-19 pandemic while this Ph.D. research was ongoing provided a similar application field to ARDS, and made further ML research in medical diagnosis applications possible. Finally, we leverage the available Modular Supercomputing Architecture (MSA) developed as part of the Dynamical Exascale Entry Platform~- Extreme Scale Technologies (DEEP-EST) EU Project to scale up and speed up the modelling processes. This Ph.D. Project is one element of the Smart Medical Information Technology for Healthcare (SMITH) project wherein the thesis research can be validated by clinical and medical experts (e.g. Uniklinik RWTH Aachen).
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001008816 8564_ $$uhttps://opinvisindi.is/handle/20.500.11815/4261
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001008816 9141_ $$y2023
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