ArticleComputational and mathematical methods in medicine2022
Construction of Prediction Model of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for renal injury in children with IgA vasculitis: a systematic review and meta-analysis.Pediatric rheumatology online journal · 2025Pooled it
- Logistic Regression and Machine Learning Algorithms for the Risk Prediction of Perioperative Adverse Cardiovascular Events in Elderly Patients.Aging medicine (Milton (N.S.W)) · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura.Journal of multidisciplinary healthcare · 2025Article
- Biomarker research for Henoch-Schönlein purpura nephritis based on "omics" techniques.Frontiers in medicine · 2025Review
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Predicting renal damage in children with IgA vasculitis by machine learning.Pediatric nephrology (Berlin, Germany) · 2024Article
- Ang-1, Ang-2, and Tie2 are diagnostic biomarkers for Henoch-Schönlein purpura and pediatric-onset systemic lupus erythematous.Open life sciences · 2024Article
- Development and validation of nomogram prediction model for severe kidney disease in children with Henoch-Schönlein purpura: A prospective analysis of two independent cohorts-forecast severe kidney disease outcome in 2,480 hospitalized Henoch-Schönlein purpura children.Frontiers in immunology · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: The children with Henoch-Schönlein purpura (HSP) may suffer from renal insufficiency, which seriously affects the life and health of the children. This study aims to construct a prediction model of Henoch-Schönlein purpura nephritis (HSPN). Methods: A total of 240 children with HSP treated in dermatology and pediatrics in our hospital were selected. The general information, patients' clinical symptoms, and laboratory examination indicators were collected for feature selection, and the XGBoost algorithm prediction model was built. Results: According to the input feature indexes, the top ten crucial feature indicators output by the XGBoost model were urine N-acetyl- Conclusion: The prediction model based on XGBoost is used to predict HSP renal damage based on clinical data of children, which can reduce the harm caused by invasive examination for patients.
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