ArticleFrontiers in neurology2022
A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 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
- Genetic Ancestry and Colorectal Cancer in the All of Us Dataset.JAMA network open · 2026Article
- Risk factors to guide Terson syndrome screening after aneurysmal subarachnoid Hemorrhage.Neurosurgical review · 2026Article
- Preoperative systemic inflammation response index may predict postoperative delayed extubation for elderly patients with aSAH: a retrospective cohort study.BMC neurology · 2025Article
- Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye.Journal of clinical medicine · 2025Article
- Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment.Therapeutics and clinical risk management · 2025Article
- Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.Aging · 2024Article
- Development and Validation of a Claims-Based Model to Predict Categories of Obesity.American journal of epidemiology · 2024Article
- Article
- Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients.BMC surgery · 2023Article
- Preoperative prognostic nutrition index can independently predict the 6-month prognosis of elderly patients undergoing neurosurgical clipping for aneurysmal subarachnoid hemorrhage.Neurosurgical review · 2023Article
- Identification of risk factors for infection after mitral valve surgery through machine learning approaches.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Backgrounds: As a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop the conventional regression and tree-based models and determine which model has better prediction performance for DCI development in hospitalized elderly patients after aSAH. Methods: This was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70-30%. One conventional regression and tree-based model, such as least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), was developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% Results: There were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in the model internal validation, DT of 0.836 (95% Conclusions: The LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.
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