Prediction of hospitalization due to heart diseases by supervised learning methods

By Wuyang Dai, Theodora S. Brisimi, William G. Adams, Theofanie Mela, Venkatesh Saligrama, Ioannis Ch. Paschalidis. 2014

140 citations recorded by the source corpus.

Background In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 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 heart diseases being responsible for about 31% of that amount. Methods Our goal is to accurately and efficiently predict heart-related hospitalizations based on the available patient-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 patients with heart diseases. Patients 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 naïve 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.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Health Information Management

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Citations in the corpus

Federated learning of predictive models from federated Electronic Health Records (2018, background): “…Support Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea…”
Explaining machine learning classifiers through diverse counterfactual explanations (2019, background): “Similar to the loan example, this argument is valid for a range of scenarios involving decision-making on an individual’s outcome, such as deciding admission to a university [40], screening job applicants [33], disbursing government aid [3, 5], and identifying people at high risk of a future disease”
Predicting diabetes-related hospitalizations based on electronic health records (2018, methodology): “[12] Predictive methods, in particular, have been used for example in the context of heart-related problems,[13–15] hemodialysis,[16] diabetes in older adults,[17–20] and multiple disease prediction.”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, methodology): “Better cardiac prediction has been achieved by adding variables available in the EHR that were not included in the traditional prediction models (32, 63, 93, 133).”
Using EHRs and Machine Learning for Heart Failure Survival Analysis (2015, unknown): “For example, Dai et al. [6] used boosting and support vector machine (SVM) schemes to build models to predict heart failure around six months before the actual diagnosis.”