A framework to predict outcome for cancer patients using data from a nursing EHR
With the rapid growth of electronic data repositories in diverse application domains, including healthcare, considerable research interest has been developed to solve issues related to extraction of hidden knowledge in these repositories. Electronic health record systems (EHRs) are the fastest growing in terms of size and data diversity. In this work, we focus on mining a high dimensional sparse dataset using nursing care data as an exemplar. To mine a high-dimensional and sparse dataset is a challenging task due to a number of reasons. There are several dimension reduction methods, however, they do not work well with contextual datasets. In our study, we have used association mining as a dimension reduction step and for extracting important features from the dataset. Our results show that association mining can be effectively used for dimension reduction and feature extraction step. Our predictive modeling results show that decision tree models generally have high accuracy and the results are easy to interpret and determine the influence of different variables.