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Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution

Carlos Sáez1,2, Montserrat Robles1, Juan M García-Gómez1,3,4

  • 11 Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, 46022 València, Spain.

Statistical Methods in Medical Research
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Summary

This study introduces novel metrics to assess stability across multiple biomedical data sources, ensuring data quality and reliable research outcomes. These methods address variability challenges in complex datasets.

Keywords:
data qualitydata reusedata variabilityinformation geometryprobability distribution distances

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Area of Science:

  • Biomedical data analysis
  • Data quality assessment
  • Statistical modeling

Background:

  • Biomedical data often originates from diverse sources, introducing potential biases and variability in probability distribution functions (PDFs).
  • Uncontrolled variability can lead to inaccurate or unreproducible research findings, particularly with complex multi-modal, multi-type, or multi-variate data.
  • Classical statistical methods may struggle to detect these subtle variabilities.

Purpose of the Study:

  • To propose novel metrics for assessing the stability of multiple data sources in biomedical research.
  • To enhance data quality assessment by quantifying multi-source variability.
  • To provide robust tools for analyzing data from distinct origins, improving research reliability.

Main Methods:

  • Development of two new metrics: global probabilistic deviation and source probabilistic outlyingness.
  • Metrics are designed to be robust to multi-modal, multi-type, and multi-variate data characteristics.
  • Utilized Jensen-Shannon distances to construct a simplex geometrical structure for metric calculation.

Main Results:

  • The proposed metrics provide bounded estimations of global multi-source variability and individual source dissimilarity.
  • Demonstrated effectiveness on simulated data and real-world biomedical data (UCI Heart Disease dataset).
  • Metrics successfully identified and quantified variability in multi-source data.

Conclusions:

  • The developed metrics offer a robust approach to biomedical data quality assessment.
  • Improved data stability analysis can enhance the efficiency and effectiveness of biomedical data exploitation.
  • These tools contribute to more accurate and reproducible biomedical research.