ArticleBMC geriatrics2023
XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban.
Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
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- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
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- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.European journal of clinical pharmacology · 2026Article
- Integrating clinical features withBMC medical imaging · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Novel machine-learning model for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.Annals of gastroenterological surgery · 2025Article
- Bioinformatics Identification of angiogenesis-related biomarkers and therapeutic targets in cerebral ischemia-reperfusion.Scientific reports · 2024Article
- Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023.Heliyon · 2024Review
- Machine Learning and Artificial Intelligence in PK-PD Modeling: Fad, Friend, or Foe?Clinical pharmacology and therapeutics · 2024Article
- Digital Technology Applications in the Management of Adverse Drug Reactions: Bibliometric Analysis.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Development of a machine learning algorithm to predict the residual cognitive reserve index.Brain communications · 2024Article
- Machine learning models for prediction of lymph node metastasis in patients with T1b gastric cancer.American journal of cancer research · 2024Article
- Intelligent tongue and facial image analysis for noninvasive prediction of glucolipid metabolic disorders.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHemorrhage is a potential and serious adverse drug reaction, especially for geriatric patients with long-term administration of rivaroxaban. It is essential to establish an effective model for predicting bleeding events, which could improve the safety of rivaroxaban use in clinical practice.
methodsThe hemorrhage information of 798 geriatric patients (over the age of 70 years) who needed long-term administration of rivaroxaban for anticoagulation therapy was constantly tracked and recorded through a well-established clinical follow-up system. Relying on the 27 collected clinical indicators of these patients, conventional logistic regression analysis, random forest and XGBoost-based machine learning approaches were applied to analyze the hemorrhagic risk factors and establish the corresponding prediction models. Furthermore, the performance of the models was tested and compared by the area under curve (AUC) of the receiver operating characteristic (ROC) curve.
resultsA total of 112 patients (14.0%) had bleeding adverse events after treatment with rivaroxaban for more than 3 months. Among them, 96 patients had gastrointestinal and intracranial hemorrhage during treatment, which accounted for 83.18% of the total hemorrhagic events. The logistic regression, random forest and XGBoost models were established with AUCs of 0.679, 0.672 and 0.776, respectively. The XGBoost model showed the best predictive performance in terms of discrimination, accuracy and calibration among all the models.
conclusionAn XGBoost-based model with good discrimination and accuracy was built to predict the hemorrhage risk of rivaroxaban, which will facilitate individualized treatment for geriatric patients.
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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 Socratic graph.