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This summary is machine-generated.

Equiflow, an open-source tool, visualizes how participant selection in clinical studies changes data composition, revealing hidden biases. This promotes transparency and equitable artificial intelligence (AI) in healthcare.

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Area of Science:

  • Clinical Research Methodology
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • Exclusion criteria and data preprocessing in clinical research can introduce hidden biases, affecting study validity and generalizability, especially in AI/ML.
  • Traditional reporting often obscures how sample composition changes during participant selection.

Purpose of the Study:

  • To introduce Equiflow, an open-source Python package for automated creation of enhanced participant flow diagrams.
  • To quantify distributional shifts and visualize changes in key variables during participant selection.

Main Methods:

  • Developed Equiflow, an open-source Python package.
  • Automated the creation of participant flow diagrams tracking sample size and composition.
  • Quantified distributional shifts and visualized variable evolution at each exclusion step.
  • Applied Equiflow to a sepsis patient cohort from the eICU database.

Main Results:

  • Sequential exclusions in a sepsis cohort reduced the sample size from 126,750 to 1,094 patients.
  • Requiring non-missing troponin measurements caused significant, typically invisible, demographic shifts.
  • Equiflow visualizations revealed substantial compositional biases introduced during cohort construction.

Conclusions:

  • Equiflow enhances transparency in clinical research by making compositional biases visible before AI/ML modeling.
  • Enables informed decisions on analyses and reporting of generalizability limitations.
  • Supports the development of more equitable clinical AI systems in data-driven healthcare.