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Latent topic ensemble learning for hospital readmission cost reduction

机译：潜在主题集成学习可降低住院率

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摘要

Unplanned hospital readmission is a costly problem in the United States. Patients treated and readmitted within 30 days cost tax payers up to $26 billion annually. In 2013 the U.S. federal government began to reduce payments to hospitals with excessive patient readmissions. Predictive modeling using machine learning can be a useful tool to help identify patients most likely to need readmission. However, current systems have several shortcomings. When creating predictive models for hospital readmission, existing methods either build models using data from a single hospital or naively combining data from multiple hospitals. Because hospitals often have different data distributions, models created from a single hospital's data are often biased. Additionally, models created from combined data overlook local data distributions. In this paper, we propose, LTEL, which uses an ensemble of topic specific models to leverage data from multiple hospitals. LTEL creates models based on latent topics derived from different hospitals. Models are built and evaluated incorporating federal financial penalties. The dataset contains data collected from 16 regional hospitals. Compared to baseline methods, LTEL significantly outperforms the best performing baseline method for cost reduction.
机译：在美国，计划外的医院再次入院是一个代价高昂的问题。在30天内接受治疗和再次入院的患者，每年给纳税人带来的费用高达260亿美元。 2013年，美国联邦政府开始减少因患者再次入院而向医院支付的费用。使用机器学习进行预测建模可以成为帮助识别最有可能需要再次入院的患者的有用工具。但是，当前的系统有几个缺点。当创建用于医院再入院的预测模型时，现有方法要么使用来自单个医院的数据来构建模型，要么天真地组合来自多个医院的数据。由于医院通常具有不同的数据分布，因此从单个医院的数据创建的模型通常会产生偏差。此外，从组合数据创建的模型会忽略本地数据分布。在本文中，我们提出了LTEL，它使用主题特定模型的集合来利用来自多家医院的数据。 LTEL基于来自不同医院的潜在主题创建模型。建立并评估了模型，并纳入了联邦金融处罚。该数据集包含从16家地区医院收集的数据。与基准方法相比，LTEL在降低成本方面明显优于性能最佳的基准方法。

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