arXiv Machine Learning By Soyeon Park, Charmgil Hong

Missingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction

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arXiv:2607. 02938v1 Announce Type: new Abstract: Clinical time series prediction in intensive care units remains challenging due to heterogeneous physiological variables and informative missingness.

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arXiv AI
Aug 24

Curriculum-Aware Interpolate-then-Refine: Learned Physiological Time-Series Imputation under Realistic Missingness

The paper introduces Curriculum‑Aware Interpolate‑then‑Refine (CAIR), a two‑stage framework for imputing physiological time‑series data. CAIR first learns a coarse base curve with a bidirectional‑GRU interpolator and then refines it through three Transformer passes, trained under a random‑gap curriculum that mimics realistic missingness. Evaluations on continuous glucose monitoring and arterial pressure datasets show CAIR outperforms all baselines across MCAR, MAR, and NMAR mechanisms, especially for long gaps and value‑dependent dropout, while also preserving clinically relevant burden metrics.

By Yu-Chao Huang, Haochen Zhang, Nicholas Konz, Tianlong Chen