ArticleJournal of the American Medical Informatics Association : JAMIA2017
Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis.BMC medical research methodology · 2019Pooled it
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Association of blood urea nitrogen to creatinine ratio and long-term outcome among women with coronary artery bypass grafting surgery: filling gaps in female.BMC nephrology · 2025Observational
- Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction.Proceedings of machine learning research · 2025Article
- Oral blood pressure augmenting agents for intravenous vasopressor weaning.World journal of clinical cases · 2024Review
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model.Sensors (Basel, Switzerland) · 2022Article
- Early prediction of hemodynamic interventions in the intensive care unit using machine learning.Critical care (London, England) · 2021Article
- Probabilistic Machine Learning for Healthcare.Annual review of biomedical data science · 2021Review
- Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables.Critical care explorations · 2021Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.Scientific reports · 2020Article
- Early prediction of circulatory failure in the intensive care unit using machine learning.Nature medicine · 2020Article
- A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Vasopressor therapy in critically ill patients with shock.Intensive care medicine · 2019Review
- Predicting intervention onset in the ICU with switching state space models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations.
objectiveWe investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. MATERIALS AND
methodsWe considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning.
resultsThe unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean.
conclusionModels that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.
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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.