ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2009
Robust parameter extraction for decision support using multimodal intensive care data.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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.
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Who cites it
23 citing papers in PubMed, 86 citations in OpenAlex.
- CardioFM: A Multimodal Foundation Model for Joint ECG and PPG Representation Learning.Research square · 2026Article
- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 2025Review
- Assessing the Predictive Capabilities of Autoregressive Integrated Moving Average and Linear Regression Models for Acute Changes in Clinical and Selected Laboratory Parameters in Children After Cardiac Surgery in the ICU.Children (Basel, Switzerland) · 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
- 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
- Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study.JMIR biomedical engineering · 2021Article
- Data-driven curation process for describing the blood glucose management in the intensive care unit.Scientific data · 2021Article
- Uncertainty in heart rate complexity metrics caused by R-peak perturbations.Computers in biology and medicine · 2018Article
- "Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology".BMC medical informatics and decision making · 2017Article
- -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.IEEE transactions on bio-medical engineering · 2017Review
- A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates.Sensors (Basel, Switzerland) · 2016Review
- A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters.Medical & biological engineering & computing · 2016Review
- Machine Learning and Decision Support in Critical Care.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2016Article
- Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations.PloS one · 2016Article
- Informatics for neurocritical care: challenges and opportunities.Neurocritical care · 2014Review
- Developing predictive models using electronic medical records: challenges and pitfalls.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- Disease-based modeling to predict fluid response in intensive care units.Methods of information in medicine · 2013Article
- Breath-by-breath analysis of cardiorespiratory interaction for quantifying developmental maturity in premature infants.Journal of applied physiology (Bethesda, Md. : 1985) · 2012Article
- A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series.Computing in cardiology · 2011Article
- Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.
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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.