ArticleBMC medical informatics and decision making2021
Machine learning with asymmetric abstention for biomedical decision-making.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An analysis of the real world performance of an artificial intelligence based autism diagnostic.Scientific reports · 2025Article
- Selective prediction for extracting unstructured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Enabling interpretable machine learning for biological data with reliability scores.PLoS computational biology · 2023Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research.Reproductive sciences (Thousand Oaks, Calif.) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and artificial intelligence have entered biomedical decision-making for diagnostics, prognostics, or therapy recommendations. However, these methods need to be interpreted with care because of the severe consequences for patients. In contrast to human decision-making, computational models typically make a decision also with low confidence. Machine learning with abstention better reflects human decision-making by introducing a reject option for samples with low confidence. The abstention intervals are typically symmetric intervals around the decision boundary. In the current study, we use asymmetric abstention intervals, which we demonstrate to be better suited for biomedical data that is typically highly imbalanced. We evaluate symmetric and asymmetric abstention on three real-world biomedical datasets and show that both approaches can significantly improve classification performance. However, asymmetric abstention rejects as many or fewer samples compared to symmetric abstention and thus, should be used in imbalanced data.
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