Abstract：
:With the passage of recent federal legislation, many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and chronic obstructive pulmonary disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, a few have focused primarily on unstructured clinical notes. We have proposed a framework, which uses natural language processing to analyze clinical notes and predict readmission. Many algorithms within the field of data mining and machine learning exist, so a framework for component selection is created to select the best components. Naïve Bayes using Chi-Squared feature selection offers an AUC of 0.690 while maintaining fast computational times.
journal_name
IEEE J Biomed Health Informjournal_title
IEEE journal of biomedical and health informaticsauthors
Agarwal A,Baechle C,Behara R,Zhu Xdoi
10.1109/JBHI.2017.2684121subject
Has Abstractpub_date
2018-03-01 00:00:00pages
588-596issue
2eissn
2168-2194issn
2168-2208journal_volume
22pub_type
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journal_title：IEEE journal of biomedical and health informatics
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