ArticleJournal of translational medicine2025
Revolutionizing Wilson disease prognosis: a machine learning approach to predict acute-on-chronic liver failure.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- [Development and validation of a machine learning-based prediction model for fatty liver in Wilson disease].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- Article
- Microelements and biochemical biomarkers-based machine learning for predicting adverse pregnancy outcomes in Wilson's disease: risk stratification by integrating hepatic fibrosis and cerebral function.Frontiers in nutrition · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Development and validation of an interpretable machine learning model for predicting ADHD comorbidity in children with allergic rhinitis: a SHAP-based analysis.Frontiers in public health · 2026Article
- Early Identification of Endometrial Malignancy in Postmenopausal Women with Asymptomatic Endometrial Thickening: A Novel Explainable Machine Learning Model.International journal of medical sciences · 2026Article
- The gut-liver-kidney-brain axis in Wilson disease: copper speciation-flux and barrier-mediated organ crosstalk.Frontiers in immunology · 2026Review
- A machine learning model for 90-day mortality prediction in hepatitis B virus-related acute-on-chronic liver failure: the pivotal role of CALLY index.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
BACKGROUND AND
objectivesWilson disease (WD), an inherited copper metabolism disorder, is a cause of acute-on-chronic liver failure (ACLF), posing life-threatening risks due to rapid progression. This study aimed to develop a machine learning (ML)-based model to predict ACLF risk in WD patients.
methodsWe retrospectively analyzed 3692 WD patients (Leipzig score ≥ 4) from The First Affiliated Hospital of Anhui University of Chinese Medicine (2014-2024), including 104 ACLF and 104 non-ACLF cases. The original data set was randomly divided into the training and test cohorts in a ratio of 7:3. Demographic, biochemical, and ultrasound data were collected. Six ML algorithms (LR, SVM, KNN, ExtraTrees, XGBoost, LightGBM) were applied to construct a predictive model, with SHAP explaining feature importance.
resultsThe XGBoost model achieved optimal performance (AUC: 0.998, accuracy: 0.968). Key predictors included TBA, APTT, diagnosis age, onset age, Hb. Elevated TBA, APTT and diagnosis age correlated with higher ACLF risk, while reduced onset age and Hb indicated poorer outcomes. Additional parameters (TT, Cl
conclusionsThe ML-based model effectively predicts WD-ACLF risk, with XGBoost demonstrating superior performance. TBA, APTT, diagnosis age, onset age and Hb emerged as critical biomarkers, offering actionable insights for early clinical intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.