Development and Validation of a Differential Diagnosis Model for Acute Appendicitis and Henoch-Schonlein Purpura in Children

Zhan, Yishan; Wang, Min; Li, Kehao; Chen, Qiang; Li, Nuoya; Zheng, Wenjie; Zhu, Yourong; Peng, Xiaojie; Zhang, Shouhua; Tang, Qiang

doi:10.1089/ped.2021.0218

Cited by 1 publication

(1 citation statement)

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“…The present study demonstrates that the XGBoost model exhibited the highest level of prediction accuracy (accuracy ≥ 0.824, AUC ≥ 0.895). Furthermore, the XGBoost model developed in this investigation outperformed our previous logistic regression model 24 in terms of prediction accuracy and discriminatory performance. XGBoost, a gradient-boosting algorithm, is recognized as one of the most potent methodologies for constructing predictive models, and its extensive utilization in diverse medical studies is well-established.…”

Section: Cohortmentioning

confidence: 73%

Artificial intelligence differentiates abdominal Henoch‐Schönlein purpura from acute appendicitis in children

Nie,

Zhan,

et al. 2023

Int J of Rheum Dis

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ObjectiveThis study aims to construct an artificial intelligence (AI) model capable of effectively discriminating between abdominal Henoch‐Schönlein purpura (AHSP) and acute appendicitis (AA) in pediatric patients.MethodsA total of 6965 participants, comprising 2201 individuals with AHSP and 4764 patients with AA, were enrolled in the study. Additionally, 53 laboratory indicators were taken into consideration. Five distinct artificial intelligence (AI) models were developed employing machine learning algorithms, namely XGBoost, AdaBoost, Gaussian Naïve Bayes (GNB), MLPClassifier (MLP), and support vector machine (SVM). The performance of these prediction models was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve assessment, and decision curve analysis (DCA).ResultsWe identified 32 discriminative indicators (p < .05) between AHSP and AA. Five indicators, namely the lymphocyte ratio (LYMPH ratio), eosinophil ratio (EO ratio), eosinophil count (EO count), neutrophil ratio (NEUT ratio), and C‐reactive protein (CRP), exhibited strong performance in distinguishing AHSP from AA (AUC ≥ 0.80). Among the various prediction models, the XGBoost model displayed superior performance evidenced by the highest AUC (XGBoost = 0.895, other models < 0.89), accuracy (XGBoost = 0.824, other models < 0.81), and Kappa value (XGBoost = 0.621, other models < 0.60) in the validation set. After optimization, the XGBoost model demonstrated remarkable diagnostic performance for AHSP and AA (AUC > 0.95). Both the calibration curve and decision curve analysis suggested the promising clinical utility and net benefits of the XGBoost model.ConclusionThe AI‐based machine learning model exhibits high prediction accuracy and can differentiate AHSP and AA from a data‐driven perspective.

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Section: Cohortmentioning

confidence: 73%