Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.

Authors

Niklas Giesa, Rustam Zhumagambetov, Maria Sekutowitcz, Sophie K Piper, Victor Sikora, Felix Balzer, Stefan Haufe, Sebastian D Boie

Published in

Nature communications. Volume 17. Issue 1. Oct 05, 2026. Epub Oct 05, 2026.

Abstract

Previous studies evaluating imputation methods on clinical time series do not jointly account for spatial (e.g., across features) and temporal (across time) structures in missingness. We present a simulation model that induces realistic spatio-temporal missingness in three real-world clinical datasets by sampling from Markov chains. A variety of imputation methods including last observation carried forward (LOCF), linear interpolation, and spatio-temporal autoencoder (STAE) are applied to fill these missingness patterns. Finally, we evaluate the influence of time series imputations on a downstream prediction task. Here we show that deep STAEs outperform simpler baseline methods when missingness patterns lack spatio-temporal structure. In contrast, linear interpolations perform best on spatio-temporal patterns, contributing to sufficient downstream performances. This study demonstrates that simple linear imputations attain robust performances for clinical real-world data with spatio-temporal missingness. We provide an open benchmark to evaluate the effect of missingness on clinical studies and prediction tasks.

PMID:
42834041
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

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