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001     1034781
005     20250203103400.0
037 _ _ |a FZJ-2024-07535
100 1 _ |a Komeyer, Vera
|0 P:(DE-Juel1)187351
|b 0
|e Corresponding author
111 2 _ |a DGKN
|c Frankfurt am Main
|d 2024-03-06 - 2024-03-09
|w Germany
245 _ _ |a Data leakage in machine learning: A conceptual take
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
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336 7 _ |a Conference Presentation
|b conf
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520 _ _ |a 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.
536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
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536 _ _ |a DFG project G:(GEPRIS)431549029 - SFB 1451: Schlüsselmechanismen normaler und krankheitsbedingt gestörter motorischer Kontrolle (431549029)
|0 G:(GEPRIS)431549029
|c 431549029
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700 1 _ |a Patil, Kaustubh
|0 P:(DE-Juel1)172843
|b 1
|e Corresponding author
700 1 _ |a Reuter, Martin
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Wolfers, Thomas
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Li, Jingwei
|0 P:(DE-Juel1)164828
|b 4
909 C O |o oai:juser.fz-juelich.de:1034781
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
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|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5254
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914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
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980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a UNRESTRICTED

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