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@INPROCEEDINGS{Fonck:1033700,
      author       = {Fonck, Simon and Fritsch, Sebastian and Pieper, Hannes and
                      Baron, Alexander and Kowalewski, Stefan and Stollenwerk,
                      André},
      title        = {{R}etrospective {C}lassification of {ARDS} in {ICU}
                      {T}ime-series data using {R}andom {F}orest with a focus on
                      {D}ata {P}re-processing},
      volume       = {58},
      number       = {24},
      issn         = {1474-6670},
      address      = {Laxenburg},
      publisher    = {IFAC},
      reportid     = {FZJ-2024-06558},
      series       = {IFAC-PapersOnLine},
      pages        = {129 - 134},
      year         = {2024},
      abstract     = {Acute Respiratory Distress Syndrome (ARDS) is a severe lung
                      injury associated with high mortality. Epidemiological
                      studies have shown that ARDS is often diagnosed too late or
                      not at all. Artificial intelligence (AI) can help clinicians
                      identify ARDS and initiate appropriate therapy earlier.
                      Various data must be collected and processed for the
                      training of such AI methods. It is particularly important to
                      consider the data basis and describe the pre-processing
                      steps of the data, as this has a major influence on the
                      results of an AI model. A random forest algorithm is
                      proposed to automatically assess a patient’s condition for
                      compatibility with an ARDS using time-series data (like
                      vital signs, laboratory values and other parameters). We
                      emphasize the data preparation and its influence on the
                      results. The model achieved moderate to excellent results
                      depending on the preparation and dataset.},
      month         = {Sep},
      date          = {2024-09-11},
      organization  = {17th Interdisciplinary Symposium
                       Automed, Villingen-Schwenningen
                       (Germany), 11 Sep 2024 - 13 Sep 2024},
      cin          = {JSC / CASA},
      ddc          = {600},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)CASA-20230315},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001359709100023},
      doi          = {10.1016/j.ifacol.2024.11.024},
      url          = {https://juser.fz-juelich.de/record/1033700},
}