title: Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.
abstract: Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this paper, we have used a nursing EHR system to build predictive models to determine what factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of-life (EOL) care.
authors: College of Engineering, University of Illinois at Chicago, Chicago, United States.
authors: Muhammad Kamran
authors: MK
authors: Lodhi
authors: College of Engineering, University of Illinois at Chicago, Chicago, United States.
authors: Rashid
authors: R
authors: Ansari
authors: College of Engineering, University of Illinois at Chicago, Chicago, United States.
authors: Yingwei
authors: Y
authors: Yao
authors: College of Nursing, University of Florida, Gainesville, United States.
authors: Gail M
authors: GM
authors: Keenan
authors: College of Nursing, University of Florida, Gainesville, United States.
authors: Diana J
authors: DJ
authors: Wilkie
authors: College of Engineering, Illinois Institute of Technology, Chicago, United States.
authors: Ashfaq A
authors: AA
authors: Khokhar
keywords: Electronic health record (EHR); component; end-of-life (EOL); predictive modeling