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@ARTICLE{ESamadi:909773,
      author       = {E. Samadi, Moein and Kiefer, Sandra and Fritsch, Sebastian
                      Johaness and Bickenbach, Johannes and Schuppert, Andreas},
      title        = {{A} training strategy for hybrid models to break the curse
                      of dimensionality},
      journal      = {PLOS ONE},
      volume       = {17},
      number       = {9},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {FZJ-2022-03403},
      pages        = {e0274569 -},
      year         = {2022},
      abstract     = {Mechanistic/data-driven hybrid modeling is a key approach
                      when the mechanistic details of the processes at hand are
                      not sufficiently well understood, but also inferring a model
                      purely from data is too complex. By the integration of first
                      principles into a data-driven approach, hybrid modeling
                      promises a feasible data demand alongside extrapolation. In
                      this work, we introduce a learning strategy for
                      tree-structured hybrid models to perform a binary
                      classification task. Given a set of binary labeled data, the
                      challenge is to use them to develop a model that accurately
                      assesses labels of new unlabeled data. Our strategy employs
                      graph-theoretic methods to analyze the data and deduce a
                      function that maps input features to output labels. Our
                      focus here is on data sets represented by binary features in
                      which the label assessment of unlabeled data points is
                      always extrapolation. Our strategy shows the existence of
                      small sets of data points within given binary data for which
                      knowing the labels allows for extrapolation to the entire
                      valid input space. An implementation of our strategy yields
                      a notable reduction of training-data demand in a binary
                      classification task compared with different supervised
                      machine learning algorithms. As an application, we have
                      fitted a tree-structured hybrid model to the vital status of
                      a cohort of COVID-19 patients requiring intensive-care unit
                      treatment and mechanical ventilation. Our learning strategy
                      yields the existence of patient cohorts for whom knowing the
                      vital status enables extrapolation to the entire valid input
                      space of the developed hybrid model.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / SMITH -
                      Medizininformatik-Konsortium - Beitrag Forschungszentrum
                      Jülich (01ZZ1803M)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(BMBF)01ZZ1803M},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36107916},
      UT           = {WOS:000892087100079},
      doi          = {10.1371/journal.pone.0274569},
      url          = {https://juser.fz-juelich.de/record/909773},
}