  Readmission rates in the hospitals are increasingly being used as a benchmark
to determine the quality of healthcare delivery to hospitalized patients.
Around three-fourths of all hospital re-admissions can be avoided, saving
billions of dollars. Many hospitals have now deployed electronic health record
(EHR) systems that can be used to study issues that trigger readmission.
However, most of the EHRs are high dimen-sional and sparsely populated, and
analyzing such data sets is a Big Data challenge. The effect of some of the
well-known dimension reduction techniques is minimized due to presence of
non-linear variables. We use association mining as a dimension reduction method
and the results are used to develop models, using data from an existing nursing
EHR system, for predicting risk of re-admission to the hospitals. These models
can help in determining effective treatments for patients to minimize the
possibility of re-admission, bringing down the cost and increasing the quality
of care provided to the patients. Results from the models show significantly
accurate predictions of patient re-admission.
