使用辅助置信标签的稀疏多维患者建模
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels
摘要：
在这项工作中，我们专注于学习的分类模型，对病人的电子健康记录（EHR）进行推理的问题。通常，需要大量昂贵的专家监督来学习这样的模型。为了降低这种成本，我们获得了置信度标签，表明专家在她提供的类别标签中的确定程度。如果可以将有意义的置信度信息并入学习方法中，则可能需要标记更少的患者实例来学习准确的模型。此外，虽然预测的准确性对于任何推理模型都很重要，但患者模型必须是可解释的，以便临床医生可以了解模型如何做出决策。为此，我们开发了一种新型的指标学习方法，称为基于置信度的指标学习（CAMEL），它支持包含置信度标签，但也以三种方式强调可解释性。首先，我们的方法诱导稀疏性，从而产生简单的模型，只使用患者EHR的一些功能。第二，CAMEL自然产生置信度分数，临床医生在做出治疗决定时可以考虑这些分数。第三，CAMEL学习的指标诱导多维空间，其中每个维度代表临床医生可以用来评估患者的不同“因素”。在我们的实验评估中，我们在真实世界的临床数据集上显示，我们的CAMEL方法能够学习与使用相同监督的其他方法一样或更准确的模型。此外，我们还表明，当CAMEL使用置信度分数时，它能够学习模型，甚至比我们测试的其他模型更准确，同时只使用10%的训练实例。最后，我们对CAMEL学习的指标进行了定性评估，并表明它们识别并清楚地阐明了模型如何进行推理的重要因素。
英文摘要：
In this work, we focus on the problem of learning a classification model that performs inference on patient Electronic Health Records (EHRs). Often, a large amount of costly expert supervision is required to learn such a model. To reduce this cost, we obtain confidence labels that indicate how sure an expert is in the class labels she provides. If meaningful confidence information can be incorporated into a learning method, fewer patient instances may need to be labeled to learn an accurate model. In addition, while accuracy of predictions is important for any inference model, a model of patients must be interpretable so that clinicians can understand how the model is making decisions. To these ends, we develop a novel metric learning method called Confidence bAsed MEtric Learning (CAMEL) that supports inclusion of confidence labels, but also emphasizes interpretability in three ways. First, our method induces sparsity, thus producing simple models that use only a few features from patient EHRs. Second, CAMEL naturally produces confidence scores that can be taken into consideration when clinicians make treatment decisions. Third, the metrics learned by CAMEL induce multidimensional spaces where each dimension represents a different "factor" that clinicians can use to assess patients. In our experimental evaluation, we show on a real-world clinical data set that our CAMEL methods are able to learn models that are as or more accurate as other methods that use the same supervision. Furthermore, we show that when CAMEL uses confidence scores it is able to learn models as or more accurate as others we tested while using only 10% of the training instances. Finally, we perform qualitative assessments on the metrics learned by CAMEL and show that they identify and clearly articulate important factors in how the model performs inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
