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Machine learning (ML) models deployed in healthcare systems must face datadrawn from continually evolving environments. However, researchers proposingsuch models typically evaluate them in a time-agnostic manner, splittingdatasets according to patients sampled randomly throughout the entire studytime period. This work proposes the Evaluation on Medical Datasets Over Time(EMDOT) framework, which evaluates the performance of a model class acrosstime. Inspired by the concept of backtesting, EMDOT simulates possible trainingprocedures that practitioners might have been able to execute at each point intime and evaluates the resulting models on all future time points. Evaluatingboth linear and more complex models on six distinct medical data sources(tabular and imaging), we show how depending on the dataset, using allhistorical data may be ideal in many cases, whereas using a window of the mostrecent data could be advantageous in others. In datasets where models sufferfrom sudden degradations in performance, we investigate plausible explanationsfor these shocks. We release the EMDOT package to help facilitate further worksin deployment-oriented evaluation over time.

Evaluating Model Performance in Medical Datasets Over Time