Hidden Data Challenges in Clinical Machine Learning

The paper highlights hidden data challenges in clinical machine learning, focusing on how commonly reused preprocessing pipelines for electronic health records embed assumptions about data availability, episode definitions, and missingness. Using MIMIC-IV, the authors identify violations in temporal, semantic, and population alignment—leading to temporal leakage, feature-task misalignment, and biased imputation—and show that these ambiguities can alter model signals without affecting overall predictive performance. They provide practical recommendations and open-source code for more temporally valid, clinically aligned, and reproducible preprocessing.

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