BACKGROUND: In 2008, the United States spent  2.2 trillion for healthcare, which was 15.5% of its @entity3006 . 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that nearly  30.8 billion in hospital care cost during 2006 was potentially preventable, with @entity299 being responsible for about 31% of that amount. METHODS: Our goal is to accurately and efficiently predict heart-related hospitalizations based on the available @entity1 -specific medical history. To the best of our knowledge, the approaches we introduce are novel for this problem. The prediction of hospitalization is formulated as a supervised classification problem. We use de-identified Electronic Health Record (EHR) data from a large urban hospital in Boston to identify @entity1 with @entity299 . @entity1 are labeled and randomly partitioned into a training and a test set. We apply five machine learning algorithms, namely Support Vector Machines (SVM), AdaBoost using trees as the weak learner, logistic regression, a na  ve Bayes event classifier, and a variation of a Likelihood Ratio Test adapted to the specific problem. Each model is trained on the training set and then tested on the test set. RESULTS: All five models show consistent results, which could, to some extent, indicate the limit of the achievable prediction accuracy. Our results show that with under 30% false alarm rate, the detection rate could be as high as 82%. These accuracy rates translate to a considerable amount of potential savings, if used in practice.

Prediction of hospitalization due to XXXX by supervised learning methods.

@entity3006 :: ('MESH:D006153', 'Chemical') :: ['GDP'] @entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'patient'] @entity299 :: ('MESH:D006331', 'Disease') :: ['heart diseases']
@entity299 :: (MESH:D006331,Disease) :: ['heart diseases']
