Minnan Xu-Wilson

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Many real-world datasets suffer from missing or incomplete data. In the healthcare setting, for example, certain patient measurement parameters, such as vitals and/or lab values, may be missing due to insufficient monitoring. When present, however, these features could be highly discriminative in predicting aspects of patient state. Therefore, it is(More)
Despite advances in adult electrocardiography (ECG) and signal processing techniques, the analysis of fetal ECGs (fECG) is still in its infancy. The clinical potential of abdominal fECG monitoring by placing electrodes over mother's abdomen in antepartum (prior to labor) has been hampered by difficulties in obtaining a reliable fECG. We propose an algorithm(More)
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree boosting. We further combine these(More)
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