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Improving structural medical process comparison by exploiting domain knowledge and mined information.

Stefania Montani1, Giorgio Leonardi1, Silvana Quaglini2

  • 1Dipartimento di Scienze e Innovazione Tecnologica, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, I-15121 Alessandria, Italy.

Artificial Intelligence in Medicine
|August 5, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for process mining and comparison in healthcare, improving accuracy in analyzing hospital processes and patient outcomes. The developed metric outperforms existing methods, aiding clinical decision-making.

Keywords:
Graph edit distanceProcess mining and comparisonStroke management

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Area of Science:

  • Healthcare Informatics
  • Process Mining
  • Clinical Data Analysis

Background:

  • Process model comparison is crucial for medical applications like conformance checking and hospital ranking.
  • Existing methods lack domain-specific tailoring for medical process analysis.

Purpose of the Study:

  • To present a framework for mining and comparing medical process models.
  • To enhance similarity quantification for medical applications, specifically stroke management.

Main Methods:

  • Utilized process mining to extract process models from hospital execution traces.
  • Modified a structural similarity metric with domain knowledge and temporal data for medical suitability.

Main Results:

  • The tailored metric outperformed the original metric, rating 11/15 models correctly versus 7/15.
  • Demonstrated that increased process model distance correlates with decreased average patient improvement in stroke care.

Conclusions:

  • Process mining and tailored comparison metrics are effective for analyzing clinical data and understanding process impact on outcomes.
  • Future work aims to generalize and improve metric efficiency for broader applications.