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@PHDTHESIS{Barakat:1008816,
      author       = {Barakat, Chadi},
      title        = {{D}esign and {E}valuation of {P}arallel and {S}calable
                      {M}achine {L}earning {R}esearch in {B}iomedical {M}odelling
                      {A}pplications},
      school       = {Háskóli Íslands},
      type         = {Dissertation},
      address      = {Reykjavík, Iceland},
      reportid     = {FZJ-2023-02488},
      isbn         = {978-9935-9697-9-8},
      pages        = {xxiv, 108},
      year         = {2023},
      note         = {Dissertation, Háskóli Íslands, 2023},
      abstract     = {The 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).},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / BMBF 01ZZ1803B - SMITH -
                      Medizininformatik-Konsortium - Beitrag Universitätsklinikum
                      Aachen (01ZZ1803B) / EUROCC - National Competence Centres in
                      the framework of EuroHPC (951732) / RAISE - Research on AI-
                      and Simulation-Based Engineering at Exascale (951733) /
                      DEEP-EST - DEEP - Extreme Scale Technologies (754304)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(BMBF)01ZZ1803B /
                      G:(EU-Grant)951732 / G:(EU-Grant)951733 /
                      G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2023-02488},
      url          = {https://juser.fz-juelich.de/record/1008816},
}