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Will You Be in Hospital Next Year : Leveraging Machine Learning in Improving Healthcare

According to some recent study from the American Hospital Association, more than 71 million individuals in the United States are admitted to hospitals each year. However, a considerable number of hospital admissions turn out to be unnecessary, and studies have shown that much money (e.g., over $30 billion in 2006) has been wasted on such unnecessary admissions. In this paper, we study the problem of predicting how many days a patient will spend in a hospital in the next year. Such prediction is valuable, since once known, health care providers can develop new care plans and strategies to reach patients before emergencies occur, thereby reducing the number of unnecessary hospitalizations. We focus on leveraging machine learning algorithms in this study. Specifically, we model the predictive problem in two different ways, either as a multi-class classification task, or as a regression task. We discuss the details of the algorithms we employ for each aspect, and present results from an extensive experimental evaluation.