ArticleNeuropsychiatric disease and treatment2022
XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage.
Article in Neuropsychiatric disease and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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47 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2026Article
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- An explainable machine learning model for identifying the risk of suboptimal oocyte maturation in patients with polycystic ovary syndrome undergoing GnRH-antagonist protocol.Journal of ovarian research · 2026Article
- A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- The impact of feature combinations on machine learning models for in-hospital mortality prediction.Scientific reports · 2025Article
- A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features.Scientific reports · 2025Article
- Identification of immunoheterogeneous key prognostic protein ZC3H12D in OSCC and analysis of prognostic models and drug resistance.Discover oncology · 2025Article
- Clinical Characteristics of Patients With Respiratory Infections After Nonpharmacological Interventions for COVID-19 in China Have Ended: Using Machine Learning Approaches to Support Pathogen Prediction at Admission.Immunity, inflammation and disease · 2025Article
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.BMC medical informatics and decision making · 2025Article
- Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes.Neurosurgical review · 2025Article
- ConsAMPHemo: A computational framework for predicting hemolysis of antimicrobial peptides based on machine learning approaches.Protein science : a publication of the Protein Society · 2025Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Article
- Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.Scientific reports · 2025Article
- Prediction Model for Risk of Death in Elderly Critically Ill Patients with Kidney Failure.Medicina (Kaunas, Lithuania) · 2025Article
- Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.BMC pregnancy and childbirth · 2025Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients suffered aneurysmal subarachnoid hemorrhage (aSAH) usually develop poor survival and functional outcome. Evaluating aSAH patients at high risk of poor outcome is necessary for clinicians to make suitable therapeutical strategy. This study is conducted to develop prognostic model using XGBoost (extreme gradient boosting) algorithm in aSAH. Methods: A total of 351 aSAH patients admitted to West China hospital were identified. Patients were divided into training set and test set with ratio of 7:3 to testify the predictive value of XGBoost based prognostic model. Additionally, logistic regression model was also constructed and compared with XGBoost based model. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity were calculated to evaluate the value of XGBoost and logistic regression. Results: There were 74 (21.1%) non-survivors and 148 (42.1%) patients with unfavorable functional outcome. Non-survivors had older age (p=0.025), lower Glasgow coma scale (GCS) (p<0.001), higher World Federation of Neurosurgical Societies WFNS score (p<0.001), mFisher score (p<0.001). The incidence of intraventricular hemorrhage (IVH) (p=0.025) and delayed cerebral ischemia (DCI) (p<0.001) was higher in non-survivors than survivors. The AUC of XGBoost model for predicting mortality and unfavorable functional outcome were 0.950 and 0.958, which were higher than 0.767 and 0.829 of logistic regression model. Conclusion: XGBoost based model is more precise than logistic regression model in predicting outcome of aSAH patients. Using XGBoost prognostic model is helpful for clinicians to identify high-risk aSAH patients and therefore strengthen medical care.
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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.