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To obtain unadjusted associations between individual predictors and outcomes, we calculated linear models with each predictor as the only independent variable, based on data completed using K-nearest neighbors imputation (KNNImputer class of the scikit-learn toolbox for Python [29] with default parameters) and weighted with population sample weights.
JMIR Public Health Surveill 2025;11:e60125
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K-means clustering, an unsupervised machine learning approach, was used to group ICD-10 codes according to hospital admission outcomes. Each presentation was categorized by its primary diagnosis, with only one diagnosis analyzed per presentation. Admission risk was calculated for each ICD-10 code and grouped into clusters.
J Med Internet Res 2025;27:e73758
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