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Mining and exploring care pathways from electronic medical records with visual analytics.

Adam Perer1, Fei Wang2, Jianying Hu1

  • 1IBM T.J. Watson Research Center, 1101 Kitchawan Road, P.O. Box 218, Yorktown Heights, NY 10598, USA.

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|July 7, 2015
PubMed
Summary

Care Pathway Explorer analyzes electronic medical record (EMR) data to identify common patient care pathways. This system reveals insights into diagnosis and treatment sequences, correlating them with patient outcomes.

Keywords:
Data-driven care plansFrequent sequence miningTemporal event visualizationVisual analytics

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Area of Science:

  • Health Informatics
  • Data Mining
  • Clinical Informatics

Background:

  • Electronic Medical Records (EMR) contain vast amounts of patient data.
  • Extracting meaningful insights from complex EMR data is challenging.
  • Understanding patient care pathways can improve health outcomes.

Purpose of the Study:

  • To develop a system, Care Pathway Explorer, for mining and visualizing frequent event sequences from EMR data.
  • To identify common sequences of medical events (diagnoses, treatments).
  • To investigate the correlation between these event sequences and patient outcomes.

Main Methods:

  • Utilized a frequent sequence mining algorithm adapted for EMR data properties (concurrency, multi-level detail, temporal context).
  • Developed novel overview and flow visualizations for interactive pattern exploration.
  • Applied the system to a cohort of hyperlipidemic patients with hypertension and diabetes.

Main Results:

  • Demonstrated the clinical relevance of mined patterns from EMR data.
  • Identified sequences corresponding to established clinical knowledge.
  • Discovered novel patterns potentially unknown to clinicians.

Conclusions:

  • Care Pathway Explorer integrates frequent sequence mining and advanced visualizations.
  • The system supports data-driven insights for care pathway discovery.
  • Facilitates the integration of EMR data analysis into clinical practice.