ReviewJournal of biomedical informatics2023
Deep imputation of missing values in time series health data: A review with benchmarking.
Review in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- Longitudinal Impact of Agitation and Aggression Symptoms on Societal Costs in Alzheimer's Disease: A Focus on Informal Care and Socioeconomic Disparities.International journal of geriatric psychiatry · 2026Observational
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 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
- Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Reply to letter to the editor on "Forecasting Anesthetic Depth Using Auto-Regressive Transformer in Propofol Infusion during Induction Phase".Journal of anesthesia · 2026Article
- Modeling heart rate (HR) dynamics to reconstruct missing HR data in trail running.BMC sports science, medicine & rehabilitation · 2026Article
- AI-based imputation of anti-Müllerian hormone enables robust prediction of oocyte retrieval during controlled ovarian stimulation.Journal of assisted reproduction and genetics · 2026Article
- A federated learning framework for deep imputation of missing data in heterogeneous ICU time series.Scientific reports · 2026Article
- Dynamic biomarker-based machine learning model predicts short-term treatment response in multiple myeloma.Journal of translational medicine · 2026Article
- Benchmarking imputation strategies for missing time-series data in critical care using real-world-inspired scenarios.Scientific reports · 2026Article
- Time-series ECG Imputation Using a Pattern-Based Masking Framework.medRxiv : the preprint server for health sciences · 2026Article
- Predictive Performance of Frailty Indices for Complications and Functional Recovery After Distal Radius Fracture Surgery in Patients Aged ≥65 years.Geriatric orthopaedic surgery & rehabilitation · 2026Article
- Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.BioData mining · 2025Article
- Missing data imputation of climate time series: A review.MethodsX · 2025Review
- Self-Attention-Based Deep Learning for Missing Sensor Data Imputation in Real-Time Probe Card Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- SysML: adaptive recommendation system for heterogeneous biomedical data preprocessing and modeling workflows.Briefings in bioinformatics · 2025Article
- Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.JMIR medical informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing values in MTS data. However, the evaluation of these deep methods is limited to one or two data sets, low missing rates, and completely random missing value types. This survey performs six data-centric experiments to benchmark state-of-the-art deep imputation methods on five time series health data sets. Our extensive analysis reveals that no single imputation method outperforms the others on all five data sets. The imputation performance depends on data types, individual variable statistics, missing value rates, and types. Deep learning methods that jointly perform cross-sectional (across variables) and longitudinal (across time) imputations of missing values in time series data yield statistically better data quality than traditional imputation methods. Although computationally expensive, deep learning methods are practical given the current availability of high-performance computing resources, especially when data quality and sample size are of paramount importance in healthcare informatics. Our findings highlight the importance of data-centric selection of imputation methods to optimize data-driven predictive models.
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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.