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.
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