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A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions.

Jacob S Zelko1, Justin Manjourides2,2

  • 1Department of Mathematics, Northeastern University, Boston, MA, USA.

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Summary

Ensuring accurate patient population definitions is crucial for reliable observational studies. This research introduces a tool to assess the fairness of disease definitions across diverse demographic groups, promoting equitable research practices.

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Area of Science:

  • Health Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Reliable observational studies depend on accurate patient population definitions.
  • Standardizing and implementing disease definitions across datasets presents significant challenges.
  • Ensuring fair representation of diverse demographic dynamics within patient populations is essential to avoid overgeneralization.

Purpose of the Study:

  • To present a generalized tool for assessing the fairness of disease definitions.
  • To evaluate the implementation of disease definitions using common fairness metrics.
  • To provide a robust method for examining population fairness across various characteristics.

Main Methods:

  • Developed a generalized tool to evaluate disease definition fairness.
  • Calculated fairness metrics to assess population representation.
  • Examined coarse and strongly intersecting populations across multiple demographic characteristics.
  • Utilized an OMOP Common Data Model (CDM) patient database for an example analysis.

Main Results:

  • Demonstrated a method for assessing fairness in disease definition implementation.
  • Highlighted practical workflows for applying the fairness assessment tool.
  • Provided an example analysis showcasing the tool's utility on real-world data.

Conclusions:

  • The developed tool offers a robust approach to evaluating the fairness of disease definitions.
  • This work facilitates more equitable and accurate patient population representation in observational research.
  • Future research can further refine the tool and expand its application to broader health data contexts.