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@INPROCEEDINGS{Komeyer:1034781,
      author       = {Komeyer, Vera and Patil, Kaustubh and Reuter, Martin and
                      Wolfers, Thomas and Li, Jingwei},
      title        = {{D}ata leakage in machine learning: {A} conceptual take},
      reportid     = {FZJ-2024-07535},
      year         = {2024},
      abstract     = {Symposium:Machine learning (ML) and artificial intelligence
                      (AI) are increasingly being applied to study how individual
                      differences in the brain can manifest as distinct
                      psychiatric illnesses. These models can help us establish
                      the neural correlates of mental distress and predict
                      individual-level diagnosis, symptoms, trajectories, and
                      treatment responses. To realize the full potential of these
                      models it is important to recognize their data requirements
                      as well as biases in data and modeling choices that can
                      limit applicability and insights provided by the models.
                      Biases in these models and data can lead to inaccurate and
                      unfair predictions and overlook individual variations, which
                      can have serious consequences for patients and they can
                      perpetuate and amplify existing health disparities and
                      inequalities. These biases may arise from methodological
                      choices including the neuroimaging modality and state,
                      behavioral phenotypes, data transformation, sample size,
                      population, and modeling pipelines. It is crucial to
                      carefully evaluate the risks associated with AI/ML-based
                      modeling such as biases and develop strategies to identify
                      and mitigate them. In doing so we can improve the accuracy,
                      fairness, and reliability of the predictions and ensure that
                      they benefit all patients equally. This symposium will
                      discuss opportunities and challenges related to application
                      of AI/ML in neuroimaging data from both applied and
                      conceptual perspectives.Data Leakage Talk:ML's popularity
                      stems from the promise of sample-level prediction using high
                      dimensional data. However, if not properly implemented and
                      evaluated, data-leakage in ML pipelines may result in
                      overoptimistic performance estimates and fail to generalize
                      to new data. In this talk I will discuss data-leakage
                      associated challenges and remedies.},
      month         = {Mar},
      date          = {2024-03-06},
      organization  = {DGKN, Frankfurt am Main (Germany), 6
                       Mar 2024 - 9 Mar 2024},
      subtyp        = {Invited},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      DFG project G:(GEPRIS)431549029 - SFB 1451:
                      Schlüsselmechanismen normaler und krankheitsbedingt
                      gestörter motorischer Kontrolle (431549029)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(GEPRIS)431549029},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1034781},
}