ArticleiScience2026
Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning in clinical medicine requires systematic evaluation across diverse modeling paradigms. We benchmarked 10 models, including classic machine learning, tabular deep learning, and automated machine learning (AutoML), across eight real-world clinical risk prediction datasets. Using a 10-time repeated 5-fold cross-validation protocol, we assessed discrimination, calibration, and clinical utility. Gradient boosting decision trees, particularly CatBoost, and the tabular foundation model TabPFN consistently demonstrated superior robustness, forming the top tier for performance. AutoGluon also exhibited strong competitiveness. In contrast, most other tabular deep learning models displayed significant instability. These findings indicate that advanced gradient boosting models and TabPFN represent premier strategies for building high-performance clinical risk prediction models, while AutoML offers a reliable alternative. This study provides crucial empirical guidance for clinicians and data scientists in selecting appropriate modeling strategies.
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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.