Observational studyScientific reports2023
Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.
Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- [Prevention and treatment of long-term sequelae in critically ill patients-the responsibility of intensive care medicine].Die Anaesthesiologie · 2025Pooled it
- From Fitness to Cognition: Machine-Learning Prediction of Cognitive Performance Using Physiological Parameters in Healthy Adults.Medicine and science in sports and exercise · 2026Article
- Article
- Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke.Frontiers in neurology · 2026Article
- Machine learning-driven sedation-analgesia optimization in mechanically ventilated sepsis patients: a retrospective MIMIC-IV analysis.Frontiers in pharmacology · 2026Article
- A network-driven computational framework for identifying FDA-approved drug repurposing across heterogeneous brain cancers.Frontiers in molecular biosciences · 2026Article
- Development and internal validation of machine learning-based models for predicting admission hypothermia in preterm infants: a retrospective cohort study.Frontiers in medicine · 2026Article
- Machine learning models for predicting transstenotic pressure gradient based on computed tomography angiography features.Quantitative imaging in medicine and surgery · 2025Article
- Development and multicenter validation of machine learning models for predicting postoperative pulmonary complications after neurosurgery.Chinese medical journal · 2025Article
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review.International journal of nursing sciences · 2025Review
- Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study.PLOS digital health · 2024Article
- Post intensive care syndrome: A review of clinical symptoms, evaluation, intervention.Heliyon · 2024Review
- Detection of Alzheimer's Disease Using Logistic Regression and Clock Drawing Errors.Brain sciences · 2023Article
- Demographic predictors of cognitive performance in participants of a local substance abuse recovery program.Journal of Alzheimer's disease reportsArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairment related to post intensive care syndrome (PICS-CI). We conducted a prospective observational study of ICU patients at two tertiary hospitals. A cohort of 2079 patients was screened, and finally 481 patients were included. Seven different ML models were considered, decision tree (DT), random forest (RF), XGBoost, neural network (NN), naïve bayes (NB), and support vector machine (SVM), and compared with logistic regression (LR). Discriminative ability was evaluated by area under the receiver operating characteristic curve (AUC), calibration belt plots, and Hosmer-Lemeshow test was used to assess calibration. Decision curve analysis was performed to quantify clinical utility. Duration of delirium, poor Richards-Campbell sleep questionnaire (RCSQ) score, advanced age, and sepsis were the most frequent and important candidates risk factors for PICS-CI. All ML models showed good performance (AUC range: 0.822-0.906). NN model had the highest AUC (0.906 [95% CI 0.857-0.955]), which was slightly higher than, but not significantly different from that of LR (0.898 [95% CI 0.847-0.949]) (P > 0.05, Delong test). Given the overfitting and complexity of some ML models, the LR model was then used to develop a web-based risk calculator to aid decision-making ( https://model871010.shinyapps.io/dynnomapp/ ). In a low dimensional data, LR may yield as good performance as other complex ML models to predict cognitive impairment after ICU hospitalization.
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