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@INPROCEEDINGS{Barakat:909196,
      author       = {Barakat, Chadi and Fritsch, Sebastian and Sharafutdinov, K.
                      and Ingolfsson, G. and Schuppert, A. and Brynjolfsson, S.
                      and Riedel, Morris},
      title        = {{L}essons learned on using {H}igh-{P}erformance {C}omputing
                      and {D}ata {S}cience {M}ethods towards understanding the
                      {A}cute {R}espiratory {D}istress {S}yndrome ({ARDS})},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03064},
      pages        = {368-373},
      year         = {2022},
      comment      = {2022 45th Jubilee International Convention on Information,
                      Communication and Electronic Technology (MIPRO) :
                      [Proceedings] - IEEE, 2022. - ISBN 978-953-233-103-5 -
                      doi:10.23919/MIPRO55190.2022.9803320},
      booktitle     = {2022 45th Jubilee International
                       Convention on Information,
                       Communication and Electronic Technology
                       (MIPRO) : [Proceedings] - IEEE, 2022. -
                       ISBN 978-953-233-103-5 -
                       doi:10.23919/MIPRO55190.2022.9803320},
      abstract     = {Acute Respiratory Distress Syndrome (ARDS), also known as
                      noncardiogenic pulmonary edema, is a severe condition that
                      affects around one in ten-thousand people every year with
                      life-threatening consequences. Its pathophysiology is
                      characterized by bronchoalveolar injury and alveolar
                      collapse (i.e., atelectasis), whereby its patient diagnosis
                      is based on the so-called ‘Berlin Definition‘. One
                      common practice in Intensive Care Units (ICUs) is to use
                      lung recruitment manoeuvres (RMs) in ARDS to open up
                      unstable, collapsed alveoli using a temporary increase in
                      transpulmonary pressure. Many RMs have been proposed, but
                      there is also confusion regarding the optimal way to achieve
                      and maintain alveolar recruitment in ARDS. Therefore, the
                      best solution to prevent lung damages by ARDS is to identify
                      the onset of ARDS which is still a matter of research.
                      Determining ARDS disease onset, progression, diagnosis, and
                      treatment required algorithmic support which in turn raises
                      the demand for cutting-edge computing power. This paper thus
                      describes several different data science approaches to
                      better understand ARDS, such as using time series analysis
                      and image recognition with deep learning methods and
                      mechanistic modelling using a lung simulator. In addition,
                      we outline how High-Performance Computing (HPC) helps in
                      both cases. That also includes porting the mechanistic
                      models from serial MatLab approaches and its modular
                      supercomputer designs. Finally, without losing sight of
                      discussing the datasets, their features, and their
                      relevance, we also include broader selected lessons learned
                      in the context of ARDS out of our Smart Medical Information
                      Technology for Healthcare (SMITH) research project. The
                      SMITH consortium brings together technologists and medical
                      doctors of nine hospitals, whereby the ARDS research is
                      performed by our Algorithmic Surveillance of ICU (ASIC)
                      patients team. The paper thus also describes how it is
                      essential that HPC experts team up with medical doctors that
                      usually lack the technical and data science experience and
                      contribute to the fact that a wealth of data exists, but
                      ARDS analysis is still slowly progressing. We complement the
                      ARDS findings with selected insights from our Covid-19
                      research under the umbrella of the European Open Science
                      Cloud (EOSC) fast track grant, a very similar application
                      field.},
      month         = {May},
      date          = {2022-05-23},
      organization  = {2022 45th Jubilee International
                       Convention on Information,
                       Communication and Electronic Technology
                       (MIPRO), Opatija (Croatia), 23 May 2022
                       - 27 May 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / EUROCC - National
                      Competence Centres in the framework of EuroHPC (951732) /
                      SMITH - Medizininformatik-Konsortium - Beitrag
                      Forschungszentrum Jülich (01ZZ1803M)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951732 /
                      G:(BMBF)01ZZ1803M},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.23919/MIPRO55190.2022.9803320},
      url          = {https://juser.fz-juelich.de/record/909196},
}