Missing data in clinical time series is pervasive and decision-critical, arising from irregular sampling, workflow-driven measurement policies, sensor failures, and intervention-dependent monitoring. Despite extensive methodological work on imputation, practitioners still lack clear guidance on selecting approaches that are safe, interpretable, and feasible in real clinical systems. This framework presents a decision-centric view of clinical time series imputation that links missingness mechanisms--- i.e ., missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)---as well as temporal irregularity, nonstationarity, noise, class imbalance, and governance constraints to defensible method selection. We show that low reconstruction error does not guarantee clinical safety: imputation can attenuate rare events, distort uncertainty, and misalign with decision thresholds even when root mean squared error (RMSE) or mean absolute error (MAE) improves. Rather than proposing a new algorithm, this work provides practical guidance on when imputation is identifiable, when uncertainty or abstention is preferable to point reconstruction, and how evaluation must extend beyond point-wise accuracy to downstream decision impact. The framework aims to support safer integration of imputation into real-world clinical monitoring and decision support systems.
Wani et al. (Mon,) studied this question.
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