2023
DOI: 10.3389/fneur.2023.1139096
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Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study

Abstract: BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinic… Show more

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“…In this context, the deployment of machine learning (ML)—a branch of artificial intelligence celebrated for its exceptional ability to decode complex patterns in vast and detailed datasets—is crucial for developing an effective predictive model 24 . Popular ML classifiers, such as random forest (RF) and extreme gradient boosting (XGBoost), have shown their adaptability in applications from detecting intracerebral hemorrhage (ICH) to predicting outcomes in patients with sICH 25 , 26 . However, there is limited research on ML models that use noncontrast CT radiomics to predict postoperative rehemorrhage in HICH patients.…”
Section: Introductionmentioning
confidence: 99%
“…In this context, the deployment of machine learning (ML)—a branch of artificial intelligence celebrated for its exceptional ability to decode complex patterns in vast and detailed datasets—is crucial for developing an effective predictive model 24 . Popular ML classifiers, such as random forest (RF) and extreme gradient boosting (XGBoost), have shown their adaptability in applications from detecting intracerebral hemorrhage (ICH) to predicting outcomes in patients with sICH 25 , 26 . However, there is limited research on ML models that use noncontrast CT radiomics to predict postoperative rehemorrhage in HICH patients.…”
Section: Introductionmentioning
confidence: 99%