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  3. Algorithmic Fairness In Machine Learning Prediction Of Autism Using Electronic Health Records.
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Algorithmic Fairness in Machine Learning Prediction of Autism Using Electronic Health Records.

Amber M Angell1, Yongqiu Li2, Jiang Bian2

  • 1University of Southern California, Los Angeles, CA, USA.

Studies in Health Technology and Informatics
|August 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models for autism spectrum disorder (ASD) diagnosis using electronic health records (EHRs) show significant fairness issues. These models perform inequitably across sexes, highlighting bias in ASD identification.

Keywords:
Autismelectronic health recordspredictive modeling

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Area of Science:

  • Medical Informatics
  • Computational Psychiatry
  • Machine Learning in Healthcare

Background:

  • Early diagnosis of autism spectrum disorder (ASD) is crucial for intervention.
  • Machine learning (ML) applied to electronic health records (EHRs) shows promise for ASD identification.
  • Existing ASD diagnostic tools exhibit sex-based disparities, necessitating fair ML models.

Purpose of the Study:

  • To develop ML-based prediction models for ASD diagnosis using EHR data.
  • To evaluate the algorithmic fairness of these ML models across sexes.
  • To identify and quantify potential biases in ASD prediction between boys and girls.

Main Methods:

  • Retrospective case-control study design.
  • Utilized large cohorts of children with and without ASD from EHRs (70,803 ASD cases, 212,409 controls).
  • Developed logistic regression and XGBoost models, assessing performance with metrics like accuracy, recall, precision, F1-score, and AUC; fairness evaluated using equal opportunity and equalized odds.

    • Main Results:

    • ML models for ASD prediction using EHR data demonstrated significant fairness issues.
    • Performance disparities were observed between boys and girls, indicating potential biases.
    • Standard performance metrics did not fully capture the extent of algorithmic bias.

    Conclusions:

    • Current ML models for ASD diagnosis using EHRs are not equitable across sexes.
    • Algorithmic fairness assessments are essential for developing reliable ASD prediction tools.
    • Further research is needed to mitigate biases and ensure equitable ASD identification for all children.