Yunguo Yu

Clinical prediction models inherit their quality from routine-care training data, yet missingness rates and class ratios are not tied to what happens after deployment. An exploratory benchmark across three MIMIC-IV cohorts and the multi-institution eICU database splits data quality into completeness, label noise, distributional similarity, representativeness and temporal integrity, training a real model under each controlled condition and measuring held-out log-loss and calibration change. The composite moved in the expected direction in all three controlled cohorts, but combined corruptions were sub-additive rather than multiplicative, and across 21 analyzable eICU hospitals it tracked calibration but not detectably discrimination.

Clinical prediction models inherit their quality from routine-care training data, yet missingness rates and class ratios are not tied to what happens after deployment. An exploratory benchmark across three MIMIC-IV cohorts and the multi-institution eICU database splits data quality into completeness, label noise, distributional similarity, representativeness and temporal integrity, training a real model under each controlled condition and measuring held-out log-loss and calibration change. The composite moved in the expected direction in all three controlled cohorts, but combined corruptions were sub-additive rather than multiplicative, and across 21 analyzable eICU hospitals it tracked calibration but not detectably discrimination.