ArticleScientific reports2018
Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
30 citing papers in PubMed.
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Long-term impact of a quality improvement program on unplanned extubation and clinical outcomes in adult intensive care units: a 24-year single-center observational study.Frontiers in medicine · 2026Article
- Design and evaluation of a non-contact AI system for monitoring unplanned device removal in neurocritical care.BMC nursing · 2025Article
- Risk Prediction of Unplanned Extubation in Inpatients Using Random Forest and Logistic Regression Models.Journal of patient safety · 2025Article
- Article
- Prediction model for unplanned extubation of thoracoabdominal drainage tube in postoperative inpatients: a retrospective study.European journal of medical research · 2025Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data.Scientific reports · 2024Article
- An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion.Global spine journal · 2023Article
- Impact of delay extubation on the reintubation rate in patients after cervical spine surgery: a retrospective cohort study.Journal of orthopaedic surgery and research · 2023Review
- Prediction of Prednisolone Dose Correction Using Machine Learning.Journal of healthcare informatics research · 2023Article
- Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.Frontiers in medicine · 2023Article
- The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms.Critical care explorations · 2022Article
- Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records.Scientific reports · 2022Article
- Machine learning for infection risk prediction in postoperative patients with non-mechanical ventilation and intravenous neurotargeted drugs.Frontiers in neurology · 2022Article
- Article
- An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.Frontiers in medicine · 2022Article
- Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.Scientific reports · 2021Article
- Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia.Scientific reports · 2021Article
- Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.Journal of clinical medicine · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning models and conventional parameters to predict the mortality of UE patients in the ICU. A total of 341 patients with UE in ICUs of Chi-Mei Medical Center between December 2008 and July 2017 were enrolled and their demographic features, clinical manifestations, and outcomes were collected for analysis. Four machine learning models including artificial neural networks, logistic regression models, random forest models, and support vector machines were constructed and their predictive performances were compared with each other and conventional parameters. Of the 341 UE patients included in the study, the ICU mortality rate is 17.6%. The random forest model is determined to be the most suitable model for this dataset with F
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.