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@ARTICLE{Hamdan:1010545,
      author       = {Hamdan, Sami and Love, Bradley C and Polier, Georg von and
                      Weis, Susanne and Schwender, Holger and Eickhoff, Simon and
                      Patil, Kaustubh},
      title        = {{C}onfound-leakage: {C}onfound {R}emoval in {M}achine
                      {L}earning {L}eads to {L}eakage},
      journal      = {GigaScience},
      volume       = {12},
      issn         = {2047-217X},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2023-03119},
      pages        = {giad071},
      year         = {2023},
      note         = {This work was partly supported by the Helmholtz-AI project
                      DeGen (ZT-I-PF-5-078), the Helmholtz Portfolio Theme
                      “Supercomputing and Modeling for the Human Brain,” and
                      Deutsche Forschungsgemeinschaft (DFG, German Research
                      Foundation), project-ID 431549029–SFB 1451 project B05.},
      abstract     = {BackgroundMachine learning (ML) approaches are a crucial
                      component of modern data analysis in many fields, including
                      epidemiology and medicine. Nonlinear ML methods often
                      achieve accurate predictions, for instance, in personalized
                      medicine, as they are capable of modeling complex
                      relationships between features and the target.
                      Problematically, ML models and their predictions can be
                      biased by confounding information present in the features.
                      To remove this spurious signal, researchers often employ
                      featurewise linear confound regression (CR). While this is
                      considered a standard approach for dealing with confounding,
                      possible pitfalls of using CR in ML pipelines are not fully
                      understood.ResultsWe provide new evidence that, contrary to
                      general expectations, linear confound regression can
                      increase the risk of confounding when combined with
                      nonlinear ML approaches. Using a simple framework that uses
                      the target as a confound, we show that information leaked
                      via CR can increase null or moderate effects to near-perfect
                      prediction. By shuffling the features, we provide evidence
                      that this increase is indeed due to confound-leakage and not
                      due to revealing of information. We then demonstrate the
                      danger of confound-leakage in a real-world clinical
                      application where the accuracy of predicting
                      attention-deficit/hyperactivity disorder is overestimated
                      using speech-derived features when using depression as a
                      confound.ConclusionsMishandling or even amplifying
                      confounding effects when building ML models due to
                      confound-leakage, as shown, can lead to untrustworthy,
                      biased, and unfair predictions. Our expose of the
                      confound-leakage pitfall and provided guidelines for dealing
                      with it can help create more robust and trustworthy ML
                      models.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / SFB
                      1451 B05 - Einzelfallvorhersagen der motorischen
                      Fähigkeiten bei Gesunden und Patienten mit motorischen
                      Störungen (B05) (458640473)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(GEPRIS)458640473},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37776368},
      UT           = {WOS:001189196000001},
      doi          = {10.1093/gigascience/giad071},
      url          = {https://juser.fz-juelich.de/record/1010545},
}