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Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data.

Sagnik Dakshit1, Sristi Dakshit1, Ninad Khargonkar1

  • 1Computer Science, The University of Texas at Dallas, Dallas, USA.

Journal of Healthcare Informatics Research
|June 28, 2023
PubMed
Summary
This summary is machine-generated.

Bias in healthcare AI hinders acceptance. Our framework, Bias Analysis in Healthcare Time series (BAHT), reveals significant bias in datasets and models, impacting fairness and performance.

Keywords:
Bias analysisBias mitigationDecision support systemsFairnessSynthetic data

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Area of Science:

  • Machine Learning
  • Healthcare AI
  • Fairness, Accountability, and Transparency (FAccT) ML

Background:

  • Deep learning decision support systems are increasingly used in healthcare, but bias in training data and models is a major challenge.
  • Bias can be amplified in real-world deployment, leading to issues like model drift and unfair outcomes.
  • Existing research often prioritizes model development over fairness analysis in healthcare AI.

Purpose of the Study:

  • To introduce a framework for analyzing bias in healthcare time series data and deep learning models.
  • To evaluate bias in prominent electrocardiogram (ECG) and electroencephalogram (EEG) datasets.
  • To assess bias amplification by supervised learning models and investigate bias mitigation strategies.

Main Methods:

  • Developed the Bias Analysis in Healthcare Time series (BAHT) framework for graphical bias analysis.
  • Investigated bias in training and testing datasets concerning protected variables.
  • Analyzed bias amplification by supervised learning models on ECG and EEG data.
  • Experimented with bias mitigation techniques: under-sampling, over-sampling, and synthetic data augmentation.

Main Results:

  • Demonstrated the pervasive presence of bias in key healthcare time series datasets.
  • Showcased that existing bias leads to potentially unfair machine learning models.
  • Observed significant bias amplification by models, with a maximum increase of 66.66%.
  • Investigated the impact of unanalyzed bias on model drift.

Conclusions:

  • Bias in healthcare datasets is extensive and can lead to unfair AI models.
  • Bias amplification by models is a critical issue that needs addressing.
  • Proper analysis of datasets, models, and bias mitigation strategies is crucial for equitable healthcare AI delivery.