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

• URL: http://arxiv.org/abs/2605.16380v1
• Date: Mon, 11 May 2026 02:39:33 GMT
• Title: ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
• Abstract summary: Clinical time-series data are difficult to model because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns.<n>Existing approaches commonly use observation masks and time-gap information, but they do not continuously capture the decaying reliability of past observations.<n>We propose Reliability-aware Temporal Aggregation with Mamba, which reconstructs clinical time series as time-variable token sequences.
• Score: 3.5795275871379704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Existing approaches commonly use observation masks and time-gap information, but they do not continuously capture the decaying reliability of past observations or consistently organize multi-resolution information within a coherent temporal context during aggregation. To address these limitations, we propose Reliability-aware Temporal Aggregation with Mamba (ReTAMamba), which reconstructs clinical time series as time-variable token sequences, estimates observation reliability from missingness and elapsed time, and augments interval summaries with statistical descriptors. Chronological Weaving is used to integrate short- and long-term temporal information within a coherent temporal context, and a budgeted token router is applied to constrain sequence length while preserving informative summaries. Experiments on MIMIC-IV, eICU, and PhysioNet 2012 show that ReTAMamba consistently improves AUPRC over strong baselines, with average relative gains of 7.51%, 7.80%, and 10.15%, respectively. Cohort-level and patient-level analyses on eICU further showed that the learned mean decay for more dynamic signals, such as heart rate and blood pressure, was 24.3% larger than that for relatively static signals, such as laboratory test variables. These findings suggest that effective prediction in irregular clinical time series requires modeling not only what was measured, but also when and how it was observed, including information freshness and observation timeliness.

Related papers

• GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care [5.469342914844525]
  We present Attention-based Graph Learning for Intensive Care (GARLIC) model.<n>GARLIC imputes missing data through exponential learnable neural network architecture.<n>On three ICU benchmarks, GARLIC sets the new state of the art in outcome prediction.
  arXiv  Detail & Related papers  (2026-08-11T14:29:48Z)
• Missingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction [6.42984920182754]
  This work presents CISM, a Channel-Independent Spectrogram framework with a Missingness stream for clinical time series prediction.<n>Experiments on an in-hospital mortality task derived from MIMIC-IV show that CISM achieves the highest mean AUROC (0.7225), AUPRC (0.3308), and F1 (0.3808) among the compared time series, missingness-aware, vision, and time-frequency baselines.
  arXiv  Detail & Related papers  (2026-07-03T04:21:19Z)
• TimeLAVA: Learning-Agnostic Data Valuation for Time Series [48.76711740818533]
  TimeLAVA is a learning-agnostic framework that values temporal segments by their marginal contribution to minimize distributional discrepancy between evaluated and reference data.<n>We provide theoretical guarantees linking valuation to model-agnostic generalization and prove bounded sensitivity to contamination.
  arXiv  Detail & Related papers  (2026-06-17T06:16:16Z)
• MATA-Former & SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction [8.92515305370631]
  We propose a framework to align predictive modeling with clinical logic.<n>We introduce Plateau-Gaussian Soft Labeling (PSL) to reformulating binary classification into continuous multi-horizon regression.<n>Our framework demonstrates superior efficacy and robust generalization in capturing risks from text-intensive, irregular clinical time series.
  arXiv  Detail & Related papers  (2026-04-02T07:45:52Z)
• Benchmarking LLM Summaries of Multimodal Clinical Time Series for Remote Monitoring [6.415950855665798]
  Large language models (LLMs) can generate fluent clinical summaries of remote therapeutic monitoring time series.<n> Existing evaluation metrics primarily focus on semantic similarity and linguistic quality, leaving event-level correctness largely unmeasured.<n>We introduce an event-based evaluation framework for multimodal time-series summarization using the Technology-Integrated Health Management (TIHM)-1.5 dementia monitoring dataset.
  arXiv  Detail & Related papers  (2026-03-02T07:33:11Z)
• Contextual and Seasonal LSTMs for Time Series Anomaly Detection [49.50689313712684]
  We propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs)<n>CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns.<n>They consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.
  arXiv  Detail & Related papers  (2026-02-10T11:46:15Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Mind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Models [0.6554326244334866]
  VITAL is a variable-aware, large language model (LLM) based framework tailored for learning from irregularly sampled physiological time series.<n>It reprograms vital signs into the language space, enabling the LLM to capture temporal context and reason over missing values.<n>It maintains robust performance under high levels of missingness, which is prevalent in real world clinical scenarios.
  arXiv  Detail & Related papers  (2025-09-26T09:44:16Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [50.56875995511431]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
  Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
  arXiv  Detail & Related papers  (2024-02-04T13:10:51Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Self-supervised Transformer for Multivariate Clinical Time-Series with Missing Values [7.9405251142099464]
  We present STraTS (Self-supervised Transformer for TimeSeries) model. It treats time-series as a set of observation triplets instead of using the traditional dense matrix representation. It shows better prediction performance than state-of-theart methods for mortality prediction, especially when labeled data is limited.
  arXiv  Detail & Related papers  (2021-07-29T19:39:39Z)
• Multi-view Integration Learning for Irregularly-sampled Clinical Time Series [1.9639092030562577]
  We propose a multi-view features integration learning from irregular time series data by self-attention mechanism in an imputation-free manner. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations, simultaneously. We build an attention-based decoder as a missing value imputer that helps empower the representation learning of the inter-relations among multi-view observations.
  arXiv  Detail & Related papers  (2021-01-25T10:02:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.