利用学习者的知识改善医疗保健决策AI
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
摘要：
包围学习被广泛应用于机器学习（ML）中，以提高模型性能并降低决策风险。在这种方法中，来自不同学习者集合的预测被组合以获得联合决策。最近，在文献中已经探索了各种方法，用于使用集成学习来估计决策不确定性;然而，确定哪些度量更适合某些决策应用仍然是一项具有挑战性的任务。在本文中，我们研究了以下关键的研究问题，在选择的不确定性度量：当一个不确定性度量优于其他？我们通过对集成学习中两个常用的不确定性度量，即集成均值和集成方差的严格分析来回答这个问题。我们表明，在温和的假设下，集成学习器，集成平均值是最好的集成方差作为决策的不确定性度量。我们经验验证我们的假设和理论结果，通过广泛的案例研究：糖尿病视网膜病变的诊断。
英文摘要：
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learners are combined to obtain a joint decision. Recently, various methods have been explored in literature for estimating decision uncertainties using ensemble learning; however, determining which metrics are a better fit for certain decision-making applications remains a challenging task. In this paper, we study the following key research question in the selection of uncertainty metrics: when does an uncertainty metric outperforms another? We answer this question via a rigorous analysis of two commonly used uncertainty metrics in ensemble learning, namely ensemble mean and ensemble variance. We show that, under mild assumptions on the ensemble learners, ensemble mean is preferable with respect to ensemble variance as an uncertainty metric for decision making. We empirically validate our assumptions and theoretical results via an extensive case study: the diagnosis of referable diabetic retinopathy.
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