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| Home > Publications database > Correct deconfounding enables causal machine learning for precision medicine and beyond > print |
| 001 | 1034775 | ||
| 005 | 20250203103400.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1101/2024.09.20.24314055 |
| 037 | _ | _ | |a FZJ-2024-07529 |
| 100 | 1 | _ | |0 P:(DE-Juel1)187351 |a Komeyer, Vera |b 0 |e Corresponding author |
| 245 | _ | _ | |a Correct deconfounding enables causal machine learning for precision medicine and beyond |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1736258044_14437 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a Artificial intelligence holds promise for individualized medicine. Yet, predictive models in the neurobiomedical domain suffer from a lack of generalizability and replicability so that transitioning models from prototyping to clinical applications still poses challenges. Key contributors to these challenges are confounding effects; in particular the oftentimes purely statistical perspective on confounding. However, complementing these statistical considerations with causal reasoning from domain knowledge can make predictive models a tool for causal biomedical inference beyond associative insights. Such causal insights give answers to biomedical questions of how and why, arguably what most biomedical investigations ultimately seek for. Here, we suggest a 5-step approach for targeted, context-informed deconfounding. We exemplify the 5-step approach with a real-world neurobiomedical predictive task using data from the UK Biobank. The core of this approach constitutes a bottom-up causal analysis to identify a correct set of deconfounders and the appropriate deconfounding method for a given causal predictive endeavour. Using the 5-step approach to combine causal with statistical confounder considerations can make predictive models based on observational (big) data a technique comparable to Randomized Control Trials (RCTs). Through causally motivated deconfounding we aim at facilitating the development of reliable and trustworthy AI as a medical tool. In addition, we aim to foster the relevance of low performing or even null result models if they originate from a “skilful interrogation of nature”, i.e. a deconfounding strategy derived from an adequate causal and statistical analysis. Ultimately, causal predictive modelling through appropriate deconfounding can contribute to mutual recursive feedback loops of causal insights across disciplines, scales and species that enable the field to disentangle the cause-effect structure of neurobiomedical mechanisms. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5254 |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5251 |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |c POF4-525 |f POF IV |x 1 |
| 536 | _ | _ | |0 G:(GEPRIS)431549029 |a DFG project G:(GEPRIS)431549029 - SFB 1451: Schlüsselmechanismen normaler und krankheitsbedingt gestörter motorischer Kontrolle (431549029) |c 431549029 |x 2 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |0 P:(DE-Juel1)131678 |a Eickhoff, Simon |b 1 |
| 700 | 1 | _ | |0 P:(DE-Juel1)176538 |a Rathkopf, Charles |b 2 |
| 700 | 1 | _ | |0 P:(DE-Juel1)161406 |a Grefkes, Christian |b 3 |
| 700 | 1 | _ | |0 P:(DE-Juel1)172843 |a Patil, Kaustubh |b 4 |
| 700 | 1 | _ | |0 P:(DE-Juel1)185083 |a Raimondo, Federico |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.1101/2024.09.20.24314055 |
| 856 | 4 | _ | |u https://www.medrxiv.org/content/medrxiv/early/2024/09/23/2024.09.20.24314055.full.pdf |
| 909 | C | O | |o oai:juser.fz-juelich.de:1034775 |p VDB |
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| 914 | 1 | _ | |y 2024 |
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