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@ARTICLE{Eickhoff:906978,
      author       = {Eickhoff, Roman M. and Bulla, Alwin and Eickhoff, Simon B.
                      and Heise, Daniel and Helmedag, Marius and Kroh, Andreas and
                      Schmitz, Sophia M. and Klink, Christian D. and Neumann, Ulf
                      P. and Lambertz, Andreas},
      title        = {{M}achine learning prediction model for postoperative
                      outcome after perforated appendicitis},
      journal      = {Langenbeck's archives of surgery},
      volume       = {407},
      number       = {2},
      issn         = {0023-8236},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2022-01791},
      pages        = {789 - 795},
      year         = {2022},
      abstract     = {Purpose Appendectomy for acute appendicitis is one of the
                      most common operative procedures worldwide in both
                      childrenand adults. In particular, complicated (perforated)
                      cases show high variability in individual outcomes. Here, we
                      developedand validated a machine learning prediction model
                      for postoperative outcome of perforated appendicitis.Methods
                      Retrospective analyses of patients with clinically and
                      histologically verified perforated appendicitis over 10
                      yearswere performed. Demographic and surgical baseline
                      characteristics were used as competing predictors of
                      single-patient out-comes along multiple dimensions via a
                      random forest classifier with stratified subsampling. To
                      assess whether complicationscould be predicted in new,
                      individual cases, the ensuing models were evaluated using a
                      replicated 10-fold cross-validation.Results A total of 163
                      patients were included in the study. Sixty-four patients
                      underwent laparoscopic surgery, whereasninety-nine patients
                      got a primary open procedure. Interval from admission to
                      appendectomy was 9 ± 12 h and duration ofthe surgery was 74
                      ± 38 min. Forty-three patients needed intensive care
                      treatment. Overall mortality was 0.6 $\%$ and morbid-ity
                      rate was $15\%.$ Severe complications as assessed by
                      Clavien-Dindo > 3 were predictable in new cases with an
                      accuracyof $68\%.$ Need for ICU stay (> 24 h) could be
                      predicted with an accuracy of $88\%,$ whereas prolonged
                      hospitalization (greaterthan 7–15 days) was predicted by
                      the model with an accuracy of $76\%.Conclusion$ We
                      demonstrate that complications following surgery, and in
                      particular, health care system-related outcomeslike
                      intensive care treatment and extended hospitalization, may
                      be well predicted at the individual level from
                      demographicand surgical baseline characteristics through
                      machine learning approaches.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000755465600001},
      doi          = {10.1007/s00423-022-02456-1},
      url          = {https://juser.fz-juelich.de/record/906978},
}