多层次分析法预测充血性心力衰竭患者再入院风险
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
摘要：
降低充血性心力衰竭（CHF）患者在出院后30天内再入院的风险非常重要，因为这种再入院不仅费用昂贵，而且是提供者护理和治疗质量的关键指标。准确预测再入院的风险可以使医院识别高风险患者，并最终通过识别在许多情况下导致这种再入院的因素来提高护理质量。在本文中，我们研究的问题，预测风险的再入院作为一个监督学习问题，使用多层分类方法。与我们的方法相反，早期的文献没有充分地试图通过建立直接预测模型来评估30天再入院的风险值。我们首先将问题分为不同的阶段，（a）一般风险（b）60天内的风险（c）30天内的风险，然后为每个阶段构建合适的分类器，从而提高使用多层决策准确预测风险的能力。我们的方法的优点是，我们可以使用不同的分类模型的子任务，更适合各自的问题。此外，与一次性单层方法相比，可以使用不同的特征和训练数据来解决每个子任务，从而导致高度自信的诊断或风险。对Multicare Health Systems的实际医院患者记录数据的实验评估表明，与之前的尝试相比，我们的模型在预测CHF患者出院后30天内的再入院风险方面明显更好。
英文摘要：
Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients within 30 days of discharge is important because such readmissions are not only expensive but also critical indicator of provider care and quality of treatment. Accurately predicting the risk-of-readmission may allow hospitals to identify high-risk patients and eventually improve quality of care by identifying factors that contribute to such readmissions in many scenarios. In this paper, we investigate the problem of predicting risk-of-readmission as a supervised learning problem, using a multi-layer classification approach. Earlier contributions inadequately attempted to assess a risk value for 30 day readmission by building a direct predictive model as opposed to our approach. We first split the problem into various stages, (a) at risk in general (b) risk within 60 days (c) risk within 30 days, and then build suitable classifiers for each stage, thereby increasing the ability to accurately predict the risk using multiple layers of decision. The advantage of our approach is that we can use different classification models for the subtasks that are more suited for the respective problems. Moreover, each of the subtasks can be solved using different features and training data leading to a highly confident diagnosis or risk compared to a one-shot single layer approach. An experimental evaluation on actual hospital patient record data from Multicare Health Systems shows that our model is significantly better at predicting risk-of-readmission of CHF patients within 30 days after discharge compared to prior attempts.
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