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@ARTICLE{Fritsch:1026013,
      author       = {Fritsch, Sebastian Johannes and Riedel, Morris and Marx,
                      Gernot and Bickenbach, Johannes and Schuppert, Andreas},
      title        = {{D}evelopment of a machine learning model for prediction of
                      the duration of unassisted spontaneous breathing in patients
                      during prolonged weaning from mechanical ventilation},
      journal      = {Journal of critical care},
      volume       = {82},
      issn         = {0883-9441},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-03269},
      pages        = {154795},
      year         = {2024},
      abstract     = {Purpose: Treatment of patients undergoing prolonged weaning
                      from mechanical ventilation includes repeated spontaneous
                      breathing trials (SBTs) without respiratory support, whose
                      duration must be balanced critically to prevent over- and
                      underload of respiratory musculature. This study aimed to
                      develop a machine learning model to predict the duration of
                      unassisted spontaneous breathing. Materials and methods:
                      Structured clinical data of patients from a specialized
                      weaning unit were used to develop (1) a classifier model to
                      qualitatively predict an increase of duration, (2) a
                      regressor model to quantitatively predict the precise
                      duration of SBTs on the next day, and (3) the duration
                      difference between the current and following day. 61
                      features, known to influence weaning, were included into a
                      Histogram-based gradient boosting model. The models were
                      trained and evaluated using separated data sets. Results:
                      18.948 patient-days from 1018 individual patients were
                      included. The classifier model yielded an ROC-AUC of 0.713.
                      The regressor models displayed a mean absolute error of 2:50
                      h for prediction of absolute durations and 2:47 h for
                      day-to-day difference. Conclusions: The developed machine
                      learning model showed informed results when predicting the
                      spontaneous breathing capacity of a patient in prolonged
                      weaning, however lacking prognostic quality required for
                      direct translation to clinical use.},
      cin          = {JSC / CASA},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)CASA-20230315},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / SDI-S - SDI-S: Smart Data
                      Innovation Services - Experimentelle Erprobung und
                      Entwicklung von KI-Dienstverbünden für Innovationen auf
                      industriellen Daten (01IS22095D)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(BMBF)01IS22095D},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38531748},
      UT           = {WOS:001247466600001},
      doi          = {10.1016/j.jcrc.2024.154795},
      url          = {https://juser.fz-juelich.de/record/1026013},
}