  When training clinical prediction models from electronic health records
(EHRs), a key concern should be a model's ability to sustain performance over
time when deployed, even as care practices, database systems, and population
demographics evolve. Due to de-identification requirements, however, current
experimental practices for public EHR benchmarks (such as the MIMIC-III
critical care dataset) are time agnostic, assigning care records to train or
test sets without regard for the actual dates of care. As a result, current
benchmarks cannot assess how well models trained on one year generalise to
another. In this work, we obtain a Limited Data Use Agreement to access year of
care for each record in MIMIC and show that all tested state-of-the-art models
decay in prediction quality when trained on historical data and tested on
future data, particularly in response to a system-wide record-keeping change in
2008 (0.29 drop in AUROC for mortality prediction, 0.10 drop in AUROC for
length-of-stay prediction with a random forest classifier). We further develop
a simple yet effective mitigation strategy: by aggregating raw features into
expert-defined clinical concepts, we see only a 0.06 drop in AUROC for
mortality prediction and a 0.03 drop in AUROC for length-of-stay prediction. We
demonstrate that this aggregation strategy outperforms other automatic feature
preprocessing techniques aimed at increasing robustness to data drift. We
release our aggregated representations and code to encourage more deployable
clinical prediction models.
