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Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks.

Wonsuk Oh1, Michael S Steinbach2, M Regina Castro3

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary
This summary is machine-generated.

We developed Severity Encoding Variables (SEVs), a novel clinical data representation, outperforming others in regression and association mining for electronic health record analytics. SEVs enhance clinical data mining and trajectory analysis.

Keywords:
Data MiningData ScienceElectronic Health Records

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Area of Science:

  • Health Informatics
  • Data Science
  • Clinical Analytics

Background:

  • Electronic Health Record (EHR) analytics are expanding to new tasks.
  • Optimal performance requires transforming EHR data into suitable representations.
  • Existing data representations may not be ideal for all clinical analytics tasks.

Purpose of the Study:

  • To classify data representations based on characteristics.
  • To propose a new knowledge-driven representation called Severity Encoding Variables (SEVs).
  • To evaluate the suitability of representations for clinical analytics, including trajectory mining.

Main Methods:

  • Classified data representations into broad categories.
  • Developed and proposed Severity Encoding Variables (SEVs) for clinical data mining.
  • Evaluated performance of SEVs against other representations for regression and association mining.

Main Results:

  • For regression tasks, SEVs showed a statistically significant advantage over other representations.
  • SEVs improved prediction for high-risk diabetes patients by 20% compared to competing methods.
  • SEVs achieved the highest performance in association mining, effectively constraining the pattern search space.

Conclusions:

  • SEVs represent a promising advancement for clinical data mining and trajectory analysis.
  • The knowledge-driven nature of SEVs is crucial for enhancing the performance of clinical analytics.
  • Data representation is a key factor in optimizing EHR-based analytical tasks.