ArticleNPJ digital medicine2025
Development and validation of an interpretable risk prediction model for the early classification of thalassemia.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Development and Validation of an Interpretable Machine Learning Model for StagingJournal of medical Internet research · 2026Article
- Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- Development and external validation of a composite biomarker-based machine learning model for sarcopenia risk stratification in patients with cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Thalassemia is an inherited blood disorder. Current diagnostic methods mainly rely on sophisticated equipment and specifically trained technicians. This study aims to identify and genotype thalassemia by applying machine learning (ML) algorithms to routine blood parameters. This study recruited a derivation cohort of 31,311 individuals from four independent hospitals and developed eight machine learning (ML) models for the purpose. The performance of these models was compared using a set of evaluation metrics. An additional cohort of 2000 patients was recruited for external validation to assess the generalization of the models. The results demonstrated that the categorical boosting (CatBoost) model exhibited the best discriminative ability in both the training and external validation cohorts. The model was then integrated into an online platform, which holds the potential to act as an auxiliary tool for identifying and genotyping thalassemia via automatic analysis of routine blood test parameters.
Identifiers
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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.