title: Predictive Modeling for End-of-Life Pain Outcome using Electronic Health Records.
abstract: Electronic health record (EHR) systems are being widely used in the healthcare industry nowadays, mostly for monitoring the progress of the patients. EHR data analysis has become a big data problem as data is growing rapidly. Using a nursing EHR system, we built predictive models for determining what factors influence pain in end-of-life (EOL) patients. Utilizing different modeling techniques, we developed coarse-grained and fine-grained models to predict patient pain outcomes. The coarse-grained models help predict the outcome at the end of each hospitalization, whereas fine-grained models help predict the outcome at the end of each shift, thus providing a trajectory of predicted outcomes over the entire hospitalization. These models can help in determining effective treatments for individuals and groups of patients and support standardization of care where appropriate. Using these models may also lower the cost and increase the quality of end-of-life care. Results from these techniques show significantly accurate predictions.
authors: University of Illinois at Chicago, Chicago, IL, US.
authors: Muhammad K
authors: MK
authors: Lodhi
authors: University of Illinois at Chicago, Chicago, IL, US.
authors: Janet
authors: J
authors: Stifter
authors: University of Illinois at Chicago, Chicago, IL, US.
authors: Yingwei
authors: Y
authors: Yao
authors: University of Illinois at Chicago, Chicago, IL, US.
authors: Rashid
authors: R
authors: Ansari
authors: University of Florida, Gainesville, FL, US.
authors: Gail M
authors: GM
authors: Kee-Nan
authors: University of Florida, Gainesville, FL, US.
authors: Diana J
authors: DJ
authors: Wilkie
authors: Illinois Institute of Technology, Chicago, IL, US.
authors: Ashfaq A
authors: AA
authors: Khokhar
keywords: data mining; electronic health records (EHR); end-of-life (EOL); predictive modeling