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Updated: Jul 24, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women's Health

Farah Kidwai-Khan1,2, Rixin Wang1,2, Melissa Skanderson2

  • 1Yale School of Medicine, New Haven, Connecticut, USA.

Medrxiv : the Preprint Server for Health Sciences
|July 3, 2023
PubMed
Summary

Developing effective data frameworks is crucial for applying artificial intelligence (AI) in women's health research. Optimized data preparation minimizes algorithmic bias and enhances the prediction of falls and fractures.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Data Science

Background:

  • Large-scale datasets in women's health studies require robust data frameworks for advanced analytics.
  • Applying machine learning (ML) and natural language processing (NLP) necessitates optimized data preparation to mitigate algorithmic bias.

Approach:

  • Developed methods to transform raw data into a structured framework suitable for ML and NLP applications.
  • Extracted information from radiology reports into a matrix for ML-based fall prediction.
  • Utilized specialized algorithms to extract meaningful terms from dual x-ray absorptiometry (DXA) scans for fracture risk prediction.

Key Points:

  • Fall prediction accuracy was higher in women compared to men.
  • Information from radiology reports was effectively converted for ML analysis.
  • DXA scan data was processed to identify key terms for fracture risk assessment.

Conclusions:

  • The data lifecycle, from raw to analytic form, involves governance, cleaning, management, and analysis.
  • Optimal data preparation is essential to reduce algorithmic bias in AI research.
  • AI-ready data frameworks are vital for improving efficiency and reducing bias in women's health studies.