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Transform healthcare decisions with TabPFN

From rare disease predictions to clinical trial optimization, 
TabPFN handles any new prediction task in seconds.

Accelerate discovery and improve patient outcomes

TabPFN delivers state-of-the-art predictions with half the data previously needed. Where previous ML comes short, TabPFN elimates the bottlenecks of your next breakthrough.

Built for sparse medical data

Works with incomplete records, out-of-distribution data, and small sample sizes that cause traditional ML to overfit

Fine-tune for your patient population

Customize predictions for your specific demographics, protocols, and clinical contexts

One model for all use-cases

Replace dozens of specialized models with one foundation model that handles any healthcare prediction task

In-Context Learning

Get research-grade results immediately — no data prep, no model selection, no long training runs & no retraining needed

High impact use cases

Hospital Quality Assessment

This dataset contains 4,400 hospitals with quality metrics including mortality rates, safety scores, readmission rates, and patient experience data to predict hospital performance ratings for quality comparison.

TabPFN achieves optimal performance (100% normalized R²) matching all benchmark models on this regression task — demonstrating consistent, reliable predictions across hospital quality metrics.

Healthcare systems and payers can assess hospital quality continuously as new performance data arrives. Quality teams identify improvement opportunities faster and benchmark facilities without retraining models for each metric or time period.

Cardiovascular Risk Prediction: 10-Year CHD

This dataset contains 4,000 patients with demographics, behavioral factors, medical history, and vital signs to predict 10-year risk of coronary heart disease.

TabPFN achieves 99.5% ROC-AUC without tuning, significantly outperforming Random Forest (82.8%), CatBoost (80.6%), LightGBM (66.3%), and XGBoost (53.5%).

Healthcare providers can confidently stratify cardiovascular risk across patient populations. Clinical teams prioritize preventive interventions for high-risk patients and optimize resource allocation without rebuilding risk models for each patient cohort.

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