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@ARTICLE{Barakat:1008647,
      author       = {Barakat, Chadi and Sharafutdinov, Konstantin and Busch,
                      Josefine and Saffaran, Sina and Bates, Declan G. and
                      Hardman, Jonathan G. and Schuppert, Andreas and
                      Brynjólfsson, Sigurður and Fritsch, Sebastian and Riedel,
                      Morris},
      title        = {{D}eveloping an {A}rtificial {I}ntelligence-{B}ased
                      {R}epresentation of a {V}irtual {P}atient {M}odel for
                      {R}eal-{T}ime {D}iagnosis of {A}cute {R}espiratory
                      {D}istress {S}yndrome},
      journal      = {Diagnostics},
      volume       = {13},
      number       = {12},
      issn         = {2075-4418},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-02448},
      pages        = {2098},
      year         = {2023},
      abstract     = {Acute Respiratory Distress Syndrome (ARDS) is a condition
                      that endangers the lives of many Intensive Care Unit
                      patients through gradual reduction of lung function. Due to
                      its heterogeneity, this condition has been difficult to
                      diagnose and treat, although it has been the subject of
                      continuous research, leading to the development of several
                      tools for modeling disease progression on the one hand, and
                      guidelines for diagnosis on the other, mainly the “Berlin
                      Definition”. This paper describes the development of a
                      deep learning-based surrogate model of one such tool for
                      modeling ARDS onset in a virtual patient: the Nottingham
                      Physiology Simulator. The model-development process takes
                      advantage of current machine learning and data-analysis
                      techniques, as well as efficient hyperparameter-tuning
                      methods, within a high-performance computing-enabled data
                      science platform. The lightweight models developed through
                      this process present comparable accuracy to the original
                      simulator (per-parameter R2 > 0.90). The experimental
                      process described herein serves as a proof of concept for
                      the rapid development and dissemination of specialised
                      diagnosis support systems based on pre-existing generalised
                      mechanistic models, making use of supercomputing
                      infrastructure for the development and testing processes and
                      supported by open-source software for streamlined
                      implementation in clinical routines.},
      cin          = {JSC},
      ddc          = {610},
      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) /
                      RAISE - Research on AI- and Simulation-Based Engineering at
                      Exascale (951733) / SMITH - Medizininformatik-Konsortium -
                      Beitrag Forschungszentrum Jülich (01ZZ1803M) / DEEP-EST -
                      DEEP - Extreme Scale Technologies (754304)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951732 /
                      G:(EU-Grant)951733 / G:(BMBF)01ZZ1803M / G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37370993},
      UT           = {WOS:001014184900001},
      doi          = {10.3390/diagnostics13122098},
      url          = {https://juser.fz-juelich.de/record/1008647},
}