The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide people with clinical expertise with domain-relevant evidence about why a prediction should be trusted. We first design a probabilistic model that relates meaningful latent concepts to prediction targets and observed data. Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction. We present a two-step process to efficiently approximate inference: (i) estimating model parameters using variational learning, and (ii) approximating maximum a posteriori estimation of latent variables in the model using a neural network, trained with an objective derived from the probabilistic model. We demonstrate the method on the task of predicting mortality risk for patients with cardiovascular disease. Specifically, using electrocardiogram and tabular data as input, we show that our approach provides appropriate domain-relevant supporting evidence for accurate predictions.

中文翻译：

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学习用辅助证据进行预测：在临床风险预测中的应用

机器学习模型对医疗保健的影响将取决于医疗保健专业人员对这些模型所做的预测的信任程度。在本文中，我们提出一种方法，向具有临床领域专业知识的人提供与域相关的证据，说明为什么应该信任预测。我们首先设计一个概率模型，该模型将有意义的潜在概念与预测目标和观察到的数据相关联。此模型中潜在变量的推断既可以做出预测，也可以为该预测提供支持证据。我们提出了一个两步过程来有效地近似推断：（i）使用变分学习估计模型参数，以及（ii）使用神经网络近似估计模型中潜在变量的最大后验估计，以从概率模型得出的目标进行训练。我们演示了预测心血管疾病患者死亡风险的任务方法。具体来说，使用心电图和表格数据作为输入，我们表明我们的方法为准确的预测提供了与领域相关的适当支持证据。