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xgboost model  (Optum Inc)

 
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    Optum Inc xgboost model
    Xgboost Model, supplied by Optum Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/xgboost+model/model+xgboost/pm41631499-205-4-39
    Average 86 stars, based on 1 article reviews
    xgboost model - by Bioz Stars, 2026-10
    86/100 stars

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    other:

    Article Title: Recent Advances in the Application of Machine Learning Models in Metabolic Dysfunction-Associated Steatotic Liver Disease.
    Article Snippet: Metabolic Dysfunction–associated Steatotic Liver Disease (MASLD) is a prevalent liver disease worldwide, with its prevalence rising alongside the increase in metabolic syndrome (MetS), obesity and ageing.. Machine learning (ML), as a powerful analysis tool to handle and analyse massive data/information, has been employed to enhance and refine the diagnosis, risk assessment, non‐invasive screening, and treatment options against MASLD.. This review thoroughly explores the application of ML in identifying MASLD‐related genes and lipidomic biomarkers, non‐invasive screening technologies such as ultrasound and imaging, and predicting the risk of disease progression to metabolic dysfunction–associated steatohepatitis (MASH) or more advanced stages, such as cirrhosis.

    Article Title: Oral Abstracts (Abstracts 1–288)
    Article Snippet: Using the XGBoost model on patients in Optum EMR, we predicted up to 29,000 additional unidentified NASH patients per 100,000 in our cohort .

    Imaging:

    Article Title: Artificial Intelligence to Diagnose Complications of Diabetes.
    Article Snippet: Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes.. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems.. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.



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    XGBoost and SHAP modeling of clearance parameter for the Reference scenario; a. beeswarm plot of SHAP values and b. BorutaShap boxplot.

    Journal: CPT: Pharmacometrics & Systems Pharmacology

    Article Title: Stochastic Gates for Covariate Selection in Population Pharmacokinetics Modeling

    doi: 10.1002/psp4.70147

    Figure Lengend Snippet: XGBoost and SHAP modeling of clearance parameter for the Reference scenario; a. beeswarm plot of SHAP values and b. BorutaShap boxplot.

    Article Snippet: In Ogami C et al. 2021 [ ], an artificial neural network (NN) was used to model individual random effects based on covariates, with SHAP (SHapley Additive exPlanations) [ ] values employed to explain the model. Janssen A et al. 2022 [ ] employed an XGBoost [ ] model instead of a NN.

    Techniques: