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Irregularly Sampled Time Series (ISTS) include partially observed feature vectors caused by the lack of temporal alignment across dimensions and the presence of variable time intervals. Especially in medical applications, because patients' examinations depend on their health status, observations in this event-based medical time series are nonuniformly distributed. When using deep learning models to classify ISTS, most work defines the problem that needs to be solved as alignment-caused data missing or nonuniformity-caused dependency change. However, they only modeled relationships between observed values, ignoring the fact that time is the independent variable for a time series. In this paper, we emphasize that irregularity is active, time-depended, and class-associated and is reflected in the Time Pattern (TP). To this end, this paper focused on the TP of ISTS for the first time, proposing a Time Pattern Reconstruction (TPR) method. It first encodes time information by the time encoding mechanism, then imputes values from time codes by the continuous-discrete Kalman network, selects key time points by the conditional masking mechanism, and finally classifies ISTS based on the reconstructed TP. Experiments on four real-world medical datasets and three other datasets show that TPR outperforms all baselines. We also show that TP can reveal biomarkers and key time points for diseases. • Classifying irregularly sampled time series is crucial in real-world applications. • Incorporate time-depended mapping into time series classification. • Extract time patterns of irregularly sampled time series. • Propose a plug-in for time-series deep learning models. • Promote the knowledge discovery ability of medical diagnosis methods. [ABSTRACT FROM AUTHOR]