ArticleAnnals of medicine and surgery (2012)2016
Using electronic health record collected clinical variables to predict medical intensive care unit mortality.
Article in Annals of medicine and surgery (2012), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 6 registered trials, which are not on this map. Cited by 22 papers.
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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.
A Randomized Clinical Trial of a Mortality Prediction Algorithm
Subpopulation-Specific Sepsis Identification Using Machine Learning
Gram Type Infection-Specific Sepsis Identification Using Machine Learning
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
Randomized Controlled Trial of a Machine Learning Algorithm for Early Sepsis Detection
Using Clinical Treatment Data in a Machine Learning Approach for Sepsis Detection
Who cites it
22 citing papers in PubMed.
- Review
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks.Journal of healthcare informatics research · 2024Article
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Article
- Identifying Patterns of Medical Intervention in Acute Respiratory Failure: A Retrospective Observational Study.Critical care explorations · 2023Article
- The effect of digitalization of nursing forms in ICUs on time and cost.BMC nursing · 2023Article
- Risk prediction with imperfect survival outcome information from electronic health records.Biometrics · 2023Article
- Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score.Open heart · 2022Article
- Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.BMC anesthesiology · 2022Article
- Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.Medicine · 2021Observational
- OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.BMC medical informatics and decision making · 2021Article
- Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study.The Lancet. Digital health · 2021Observational
- Performance of intensive care unit severity scoring systems across different ethnicities.medRxiv : the preprint server for health sciences · 2021Article
- Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system.Einstein (Sao Paulo, Brazil) · 2021Article
- Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.Annals of medicine and surgery (2012) · 2020Article
- Article
- Early Hospital Mortality Prediction using Vital Signals.Smart health (Amsterdam, Netherlands) · 2018Article
- Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.BMJ open · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.
objectiveDevelop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.
methodsWe have developed an algorithm,
results
conclusionsThrough the multidimensional analysis of the correlations between eight common clinical variables,
Indexed as
Identifiers
What 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.