Hidden Data Challenges in Clinical Machine Learning
The study identifies how reused preprocessing pipelines in electronic health records can hide assumptions about data availability and clinical meaning. Using MIMIC-IV, the research quantified violations of temporal, semantic, and population alignment. It shows these ambiguities alter model signals even if overall performance remains unchanged, necessitating rigorous documentation for reproducible clinical machine learning.