ArticleFrontiers in aging neuroscience2022
Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants.Neurosurgical review · 2025Pooled it
- Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.Neurocritical care · 2026Article
- Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid Hemorrhage: The Role of CNS Infection-Related Parameters.Biomedicines · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Harnessing machine learning with auditory tests and demographic factors to forecast children's reading abilities in children living with and without HIV.BioData mining · 2026Article
- Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.Neurosurgical review · 2026Article
- Machine Learning-Based Prediction Model Construction for Type 2 Diabetes Mellitus: A Comparison of Algorithms and Multilevel Risk Factor Analysis.Journal of diabetes research · 2026Article
- Cerebrospinal fluid cellular and inflammatory profiles in tumor-related postoperative infections: influencing factors and clinical features in oncological patients.American journal of cancer research · 2026Article
- External multicenter validation of the eSAH score for predicting outcomes after subarachnoid hemorrhage.Scientific reports · 2025Article
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.Patient safety in surgery · 2025Review
- Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan.BMC public health · 2025Article
- Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes.Neurosurgical review · 2025Article
- Depression after aneurysmal subarachnoid hemorrhage: development of a screening tool and discharge user interface.Acta neurochirurgica · 2025Article
- Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study.Psychiatry investigation · 2025Article
- Predicting lower extremity deep venous thrombosis in patients with aneurysmal subarachnoid hemorrhage: a machine learning study.Frontiers in neurology · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- Detection of Subarachnoid Hemorrhage Using CNN with Dynamic Factor and Wandering Strategy-Based Feature Selection.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury.Clinical kidney journal · 2024Article
- Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.AJNR. American journal of neuroradiology · 2024Article
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Methods: This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training ( Results: A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73-0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68-0.9, Conclusions: In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions. Clinical Trial Registration: http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.
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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.