论文标题
在临床机器学习模型中可视化不确定性的考虑因素
Considerations for Visualizing Uncertainty in Clinical Machine Learning Models
论文作者
论文摘要
在医疗保健环境中，面向临床医生的预测模型越来越多。无论他们在性能指标方面的成功如何，所有模型都有不确定性。我们研究了如何以可行的，可信赖的方式在这种情况下视觉传达不确定性。为此，我们对心脏重症监护临床医生进行了定性研究。我们的结果表明，临床医生的信任可能最大程度不受不确定性程度，而是由于不确定性来源的可视化程度而受到影响。我们的结果显示了特征可解释性和临床可行性之间的明显联系。
Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care clinicians. Our results reveal that clinician trust may be impacted most not by the degree of uncertainty, but rather by how transparent the visualization of what the sources of uncertainty are. Our results show a clear connection between feature interpretability and clinical actionability.
