The right ventricular (RV) function deterioration strongly predicts clinical
outcomes in numerous circumstances. To boost the clinical deployment of
ensemble regression methods that quantify RV volumes using tabular data from
the widely available two-dimensional echocardiography (2DE), we propose to
complement the volume predictions with uncertainty scores. To this end, we
employ an instance-based method which uses the learned tree structure to
identify the nearest training samples to a target instance and then uses a
number of distribution types to more flexibly model the output. The
probabilistic and point-prediction performances of the proposed framework are
evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and
end-systolic RV volumes. The reference values for point performance were
obtained from MRI. The results demonstrate that our flexible approach yields
improved probabilistic and point performances over other state-of-the-art
methods. The appropriateness of the proposed framework is showcased by
providing exemplar cases. The estimated uncertainty embodies both aleatoric and
epistemic types. This work aligns with trustworthy artificial intelligence
since it can be used to enhance the decision-making process and reduce risks.
The feature importance scores of our framework can be exploited to reduce the
number of required 2DE views which could enhance the proposed pipeline's
clinical application.

提出了一种使用不确定性评分来量化二维超声心动图中右心室体积的集成回归方法，通过学习树结构来识别目标实例的最近训练样本，并使用多种分布类型更灵活地建模输出，该方法在小规模数据集上得到验证，表明其相对于其他最先进的方法具有更好的概率和点预测性能，体现了可靠的人工智能，可用于增强决策过程并降低风险，并可利用框架的特征重要性分数减少所需的二维超声心动图视图数量，从而增强了该管道的临床应用。

拥抱不确定性灵活性：利用监督树核强化集成建模以预测基于二维超声心动图的右心室容量

Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel  to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of  Right Ventricular Volume