ArticleScientific reports2018
Recurrent Neural Networks for Multivariate Time Series with Missing Values.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 323 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
323 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- Quality-Aware AIoT-Enabled Wireless Urban Sensor Networks Using Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.Cancers · 2026Review
- BioEdge-RGC: Compact Neural Policy Transfer from Model-Predictive Control in a Transcriptomics-Informed Optic Nerve Injury Simulation.Brain sciences · 2026Article
- Review
- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.Journal of healthcare informatics research · 2026Review
- Multi-Horizon Herd-Based Cattle Live-Weight Forecasting Using Irregular Automated Weighing Data.Animals : an open access journal from MDPI · 2026Article
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- Time-Aware and Power-Law Retention Gating Mechanisms in LSTMs for Irregularly Sampled Sensor Data: A Survey.Sensors (Basel, Switzerland) · 2026Review
- Article
- Spatio-Temporal Graph Autoencoder for Sensor Data Reconstruction in Vineyard Microclimate Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- Causal multi-fidelity surrogate forward and inverse models for ICF implosions.Scientific reports · 2026Article
- Bridging Machine Learning and Clinical Endpoints: A METABRIC-Informed Simulation Study of Missing Data Imputation for RECIST-Based Best Overall Response.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Across Heterogeneous Biomedical Data: Representation, Integration, and Deployable Systems.Bioengineering (Basel, Switzerland) · 2026Review
263 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.