ArticlePeerJ2019
Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07328997 (Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Clinical models for predicting 30-day mortality in ARDS: A focus on ventilatory ratio-defined subgroups.Journal of intensive medicine · 2026Article
- 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
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Multipotent mesenchymal stromal/stem cell-based therapies for acute respiratory distress syndrome: current progress, challenges, and future frontiers.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Review
- A narrative review of progress in the application of artificial intelligence in acute respiratory distress syndrome: subtypes and predictive models.Annals of translational medicine · 2023Review
- Risk of Acute Respiratory Distress Syndrome in Community-Acquired Pneumonia Patients: Use of an Artificial Neural Network Model.Emergency medicine international · 2023Article
- Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP).Diagnostics (Basel, Switzerland) · 2022Article
- Prognostic value of glucose-to-lymphocyte ratio in critically ill patients with acute respiratory distress syndrome: A retrospective cohort study.Journal of clinical laboratory analysis · 2022Article
- Optimal selection of COVID-19 vaccination sites in the Philippines at the municipal level.PeerJ · 2022Article
- Improvement of APACHE II score system for disease severity based on XGBoost algorithm.BMC medical informatics and decision making · 2021Article
- Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.Annals of translational medicine · 2021Article
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- Artificial Intelligence for Mechanical Ventilation: A Transformative Shift in Critical Care.Therapeutic advances in pulmonary and critical care medicineArticle
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.
methodsThis was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.
resultsA total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO
conclusionsThe study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.
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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.