ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2015
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
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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.
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Who cites it
47 citing papers in PubMed.
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- Longitudinal voice biomarker trajectory modelling for Parkinson's disease severity: domain-adaptive transfer learning on mPower real-world smartphone data.Frontiers in digital health · 2026Article
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
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- An empirical study of using radiology reports and images to improve intensive care unit mortality prediction.JAMIA open · 2025Article
- A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit.Computer methods and programs in biomedicine · 2024Article
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Online model selection by learning how compositional kernels evolve.Transactions on machine learning research · 2023Article
- Machine Learning for Benchmarking Critical Care Outcomes.Healthcare informatics research · 2023Article
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.Knowledge and information systems · 2023Article
- Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Entering the new digital era of intensive care medicine: an overview of interdisciplinary approaches to use artificial intelligence for patients' benefit.European journal of anaesthesiology and intensive care · 2023Article
- mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- Conditional generation of medical time series for extrapolation to underrepresented populations.PLOS digital health · 2022Article
- Article
- A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients.Journal of biomedical informatics · 2022Article
- Heteroscedastic Temporal Variational Autoencoder for Irregular Time Series.... International Conference on Learning Representations · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).
Identifiers
27182460PMC4864016What 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.