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A relative similarity based method for interactive patient risk prediction

机译：基于相对相似度的交互式患者风险预测方法

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This paper investigates the patient risk prediction problem in the context of active learning with relative similarities. Active learning has been extensively studied and successfully applied to solve real problems. The typical setting of active learning methods is to query absolute questions. In a medical application where the goal is to predict the risk of patients on certain disease using Electronic Health Records (EHR), the absolute questions take the form of "Will this patient suffer from Alzheimer's later in his/her life?", or "Are these two patients similar or not?". Due to the excessive requirements of domain knowledge, such absolute questions are usually difficult to answer, even for experienced medical experts. In addition, the performance of absolute question focused active learning methods is less stable, since incorrect answers often occur which can be detrimental to the risk prediction model. In this paper, alternatively, we focus on designing relative questions that can be easily answered by domain experts. The proposed relative queries take the form of "Is patient A or patient B more similar to patient C?", which can be answered by medical experts with more confidence. These questions poll relative information as opposed to absolute information, and even can be answered by non-experts in some cases. In this paper we propose an interactive patient risk prediction method, which actively queries medical experts with the relative similarity of patients. We explore our method on both benchmark and real clinic datasets, and make several interesting discoveries including that querying relative similarities is effective in patient risk prediction, and sometimes can even yield better prediction accuracy than asking for absolute questions.
机译：本文研究了具有相对相似性的主动学习背景下的患者风险预测问题。主动学习已得到广泛研究，并成功应用于解决实际问题。主动学习方法的典型设置是查询绝对问题。在医疗应用中，其目标是使用电子健康记录（EHR）来预测患者罹患某些疾病的风险，绝对问题采用“该患者一生中会罹患老年痴呆症吗？”或“这两个病人是否相似？”。由于领域知识的过多要求，即使对于有经验的医学专家来说，这样的绝对问题通常也很难回答。另外，由于经常出现错误的答案，这可能会损害风险预测模型，因此绝对问题导向的主动学习方法的性能不太稳定。或者，在本文中，我们专注于设计领域专家可以轻松回答的相关问题。提出的相关查询采用“患者A或患者B是否更类似于患者C？”的形式，医学专家可以更有把握地回答。这些问题会轮询相对信息，而不是绝对信息，在某些情况下甚至可以由非专家回答。在本文中，我们提出了一种交互式的患者风险预测方法，该方法可以主动向医学专家询问患者的相对相似性。我们在基准数据集和实际临床数据集上都探索了我们的方法，并做出了一些有趣的发现，包括查询相对相似性在患者风险预测中很有效，有时甚至可以比提出绝对问题产生更好的预测准确性。

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