Clinical Irregular Time-Series Classification via OPERA: Observation-Process Encoding and Residual Adaptation

In clinical irregular multivariate time series (IMTS), how often measurements are taken, when they occur, and which variables are recorded provide predictive information beyond measured values. However, existing approaches often couple observation modeling with specific architectures, limiting the flexibility to combine monitoring information with the distinct modeling strengths of different IMTS backbones. We propose Observation-Process Encoding and Residual Adaptation (OPERA), a lightweight plug-in module for clinical IMTS classification. Its Observation-Process Encoder uses observation times and variable identities, but not measured values, to construct human-readable descriptors of monitoring amount, timing, and variable selection, making monitoring patterns explicit and analyzable. The Residual Observation Gate then adaptively adds an observation-based correction to the backbone's patient representation, guided by both the monitoring pattern and the backbone representation. This patient-level interface preserves the original encoding and aggregation mechanisms, allowing the same design to complement heterogeneous IMTS backbones. Experiments on public intensive care unit (ICU) benchmarks show that OPERA consistently improves all five evaluated backbones at only marginal additional computational cost and achieves the best aggregate performance among representative methods.