ArticleFrontiers in digital health2026
Identification and validation of an explainable prediction model of favorable outcome under integrative medicine treatment exposure in DKD adult patients: a retrospective cohort study.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background: Diabetic kidney disease (DKD) shows heterogeneous responses to integrative medicine treatment (IMT). A critical unmet need in DKD management is the inability to predict IMT response, which is essential for advancing personalized treatment strategies. Aim: To develop and validate an explainable model for predicting likelihood of favorable outcome under IMT exposure in adult DKD patients. Methods: A retrospective cohort comprising 7,400 patients with diabetic kidney disease (DKD) from 2010 to 2018 was analyzed. Among them, 3,900 consecutive cases diagnosed between 2010 and 2014 were randomly divided in a 7:3 ratio into a training set ( Results: XGBoost performed best (AUC = 0.783 in training, 0.715 in test and 0.762 in validation set), with 10 key variables, namely creatinine (cr), uric acid (ua), age, red blood cell count (rbc), urea, glucose (glu), platelet count (plt), calcium (ca), white blood cell count (wbc), and sodium (Na). The web app enabled real-time prediction (https://predictionfordkd.shinyapps.io/Prediction/). Conclusion: The model effectively predicts likelihood of favorable outcome under IMT exposure in DKD, aiding personalized treatment.
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