ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction

2026-05-19 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research, Data Science & Analytics · Depth: Expert, extended

Summary

ReTAMamba, a novel model for irregular clinical time series prediction, addresses challenges like irregular sampling, missing values, and heterogeneous observation patterns in Electronic Health Record (EHR) data. It reconstructs clinical time series as time-variable token sequences, estimates observation reliability from missingness and elapsed time, and augments interval summaries with statistical descriptors. The model employs Chronological Weaving to integrate short- and long-term temporal information and a budgeted token router to manage sequence length. Evaluated on MIMIC-IV, eICU, and PhysioNet 2012 datasets for in-hospital mortality prediction, ReTAMamba consistently improved AUPRC over strong baselines, achieving average relative gains of 7.51%, 7.80%, and 10.15% respectively. Cohort-level analysis on eICU revealed that dynamic signals like heart rate had a 24.3% larger mean decay than static lab variables, indicating the model's ability to capture information freshness.

Key takeaway

For AI Scientists and Machine Learning Engineers developing predictive models for clinical time series, ReTAMamba offers a robust framework. You should consider implementing reliability-aware temporal aggregation and multi-scale tokenization, especially when dealing with sparse and irregularly sampled EHR data. This approach significantly enhances prediction accuracy for critical outcomes like in-hospital mortality, while maintaining competitive computational efficiency, making it suitable for real-time clinical decision support systems.

Key insights

ReTAMamba effectively models irregular clinical time series by integrating reliability-aware multi-scale temporal aggregation with Mamba.

Principles

• Observation reliability decays exponentially with elapsed time.
• Multi-scale temporal context improves clinical prediction.
• Budgeted token routing maintains efficiency for long sequences.

Method

ReTAMamba tokenizes irregular time series, estimates variable-specific reliability, aggregates multi-scale summaries with Chronological Weaving, and compresses sequences via budgeted token routing before Mamba encoding.

In practice

• Use variable-specific decay rates for clinical data.
• Combine short- and long-term temporal scales.
• Apply token routing to manage sequence length.

Topics

• Irregular Clinical Time Series
• Mamba Architecture
• Reliability-Aware Modeling
• Multi-scale Temporal Aggregation
• Chronological Weaving

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

Related on AIssential

• Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations — Embedding physical priors into continuous-time latent dynamics improves Remaining Useful Life prediction under irregular observations.
• Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment — Multimodal alignment of clinical text and EHR data significantly improves temporal precision in patient timeline reconstruction.
• Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning — A probabilistic deep learning framework forecasts Alzheimer's disease progression with quantified uncertainty, outperforming existing baselines.
• Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting — Multi-offset temporal embedding combined with KANs and Transformers significantly improves time series forecasting accuracy.
• MambaSL: Exploring Single-Layer Mamba for Time Series Classification — MambaSL, a single-layer Mamba variant, achieves state-of-the-art time series classification performance.
• TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning — LLM agents struggle with multi-turn, decision-oriented time series analysis due to memory, uncertainty, and domain reasoning gaps.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.