aceFrom Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions
Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups. This article presents the Fairness-Aware Data Orchestration Pipeline (FADOP), a reusable workflow that analyzes biomedical datasets, trains baseline binary classifiers, audits…
When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups.
Evidence
- Peer-reviewedComputational and Structural Biotechnology Journal2026-09-03
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Truvace Impact Record TRV-2026-0986, v1: “From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.” Truvace, 2026-09-05. /record/TRV-2026-0986 (accessed at citation time). sha256 6af5a6ffe0f5286e…
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