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Predictive Modeling for End-of-Life Pain Outcome Using Electronic Health Records

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Advances in Data Mining: Applications and Theoretical Aspects (ICDM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9165))

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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.

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Acknowledgements

This research was made possible by Grant Number 1R01 NR012949 from the National Institutes of Health, National Institute for Nursing Research (NINR). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NINR.

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Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, USA

    Muhammad K. Lodhi, Janet Stifter, Yingwei Yao & Rashid Ansari

  2. University of Florida, Gainesville, FL, USA

    Gail M. Keenan & Diana J. Wilkie

  3. Illinois Institute of Technology, Chicago, IL, USA

    Ashfaq A. Khokhar

Authors
  1. Muhammad K. Lodhi
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  2. Janet Stifter
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  3. Yingwei Yao
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  4. Rashid Ansari
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  5. Gail M. Keenan
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  6. Diana J. Wilkie
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  7. Ashfaq A. Khokhar
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Corresponding author

Correspondence to Ashfaq A. Khokhar .

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Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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Lodhi, M.K. et al. (2015). Predictive Modeling for End-of-Life Pain Outcome Using Electronic Health Records. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-20910-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20909-8

  • Online ISBN: 978-3-319-20910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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