ArticleComputing in cardiology2011
A Hypotensive Episode Predictor for Intensive Care based on Heart Rate and Blood Pressure Time Series.
Article in Computing in cardiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for predicting acute hypotension: A systematic review.Frontiers in cardiovascular medicine · 2022Pooled it
- Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis.BMC medical research methodology · 2019Pooled it
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
- Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.Critical care (London, England) · 2020Article
- A review of big data applications of physiological signal data.Biophysical reviews · 2019Review
- Hypotension in ICU Patients Receiving Vasopressor Therapy.Scientific reports · 2017Article
- Big Data Analytics in Healthcare.BioMed research international · 2015Review
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
In the intensive care unit (ICU), prompt therapeutic intervention to hypotensive episodes (HEs) is a critical task. Advance alerts that can prospectively identify patients at risk of developing an HE in the next few hours would be of considerable clinical value. In this study, we developed an automated, artificial neural network HE predictor based on heart rate and blood pressure time series from the MIMIC II database. The gap between prediction time and the onset of the 30-minute target window was varied from 1 to 4 hours. A 30-minute observation window preceding the prediction time provided input information to the predictor. While individual gap sizes were evaluated independently, weighted posterior probabilities based on different gap sizes were also investigated. The results showed that prediction performance degraded as gap size increased and the weighting scheme induced negligible performance improvement. Despite low positive predictive values, the best mean area under ROC curve was 0.934.
Identifiers
21874161PMC3162312What 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.