Key result
Machine learning algorithms applied to clinical and imaging data can generate population screening, optimal trial selection, and individualized risk prediction in severe TR.
Machine learning techniques such as supervised recursive partitioning offer novel approaches for risk stratification and patient selection in severe tricuspid regurgitation.
In a cohort of patients with severe tricuspid regurgitation (TR), supervised recursive partitioning algorithms, adjusting for clinical, laboratory, and imaging data, are used to generate: (i) population screening by pheno-clustering; (ii) optimal patient selection for clinical trials; and (iii) individualized risk prediction by decision trees. The role of machine learning in optimizing individual decisions about the timing of therapeutic interventions in TR awaits future trials.
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Chorin et al. (2023) conducted an editorial in Severe tricuspid regurgitation. Machine learning (supervised recursive partitioning algorithms) was evaluated. Machine learning algorithms applied to clinical and imaging data can generate population screening, optimal trial selection, and individualized risk prediction in severe TR.
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