Machine learning-based prediction of post-thrombotic syndrome in unprovoked lower extremity deep vein thrombosis: a two-center study incorporating uric acid and clinical risk factors

The model of machine learning shows a great capacity in prediction of PTS, and had high prediction accuracy, the SVM algorithm predicted better than other algorithms. Addition of uric acid and timing of treatment refines risk stratification. The tool developed might assist clinicians in early identification and risk stratification of patients for individualized care.