ArticleScientific data2019
Multitask learning and benchmarking with clinical time series data.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 219 papers, 6 of them syntheses 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.
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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.
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Who cites it
219 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Machine learning for the localization of Subthalamic Nucleus during deep brain stimulation surgery: a systematic review and Meta-analysis.Neurosurgical review · 2024Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.Applied clinical informatics · 2021Pooled it
- Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data.Orphanet journal of rare diseases · 2023Trial
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
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- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026Article
- Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques.Scientific reports · 2026Article
- MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data.Patterns (New York, N.Y.) · 2026Article
- Uncertainty-calibrated adaptation of clinical transformer foundation models enhances in-hospital mortality and hospital readmission prediction.npj health systems · 2026Article
- Time-Updated Prognostic Modeling in ICU Patients with Documented Coma or Unresponsiveness Using Routine Arterial Blood Gas Trajectories: An Exploratory Explainable Machine-Learning Study.Journal of clinical medicine · 2026Article
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- Machine Learning Across Heterogeneous Biomedical Data: Representation, Integration, and Deployable Systems.Bioengineering (Basel, Switzerland) · 2026Review
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- A multi-task learning approach combining regression and classification tasks for joint feature selection.Scientific reports · 2026Article
159 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
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural 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.