ArticleJournal of clinical monitoring and computing2022
Predicting the mortality risk of acute respiratory distress syndrome: radial basis function artificial neural network model versus logistic regression model.
Article in Journal of clinical monitoring and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Clinical outcome prediction in pediatric respiratory infections using hybrid feature selection and a genetic algorithm-optimized machine learning.Scientific reports · 2025Article
- Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis.Intensive care medicine experimental · 2025Review
- Prediction algorithm for ICU mortality and length of stay using machine learning.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
To predict the mortality of acute respiratory distress syndrome (ARDS) by using a radial basis function (RBF) artificial neural network (ANN) model. This study included 217 patients who were admitted between June 2013 and November 2019. The RBF ANN model and logistic regression (LR) model were based on twelve factors related to ARDS. Statistical indexes were used to determine the value of the prediction in the two models. The sensitivity, specificity and accuracy of the RBF ANN model to predict mortality were 83.6%, 88.5% and 82.5%, respectively. Significant differences were found between the RBF ANN and LR models (P < 0.05). When the RBF ANN model was used to identify ARDS, the area under the ROC curve was 0.854 ± 0.029. LDH, organ failure, SP-D and PaO2/FiO2 were the most important independent variables. The RBF ANN model was more likely to predict the mortality of ARDS than the LR model. In addition, it can extract informative risk factors for ARDS.
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.