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Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse

Kara Liu1, Russ B Altman2

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Summary

Conditional generative models (CGMs) can create patient-specific synthetic data to address limitations in medical datasets, improving precision medicine and patient care. These advanced models help overcome data diversity issues and enable the simulation of hypothetical outcomes for better research.

Keywords:
EHRsdiversityfairnessgenerative modelshealthcareprecision medicinesynthetic datatabular data

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Precision Medicine

Background:

  • Tabular medical datasets (EHRs, biobanks) are valuable but suffer from limited patient diversity and inability to simulate hypothetical outcomes.
  • These limitations hinder equitable and effective medical research, impacting precision medicine and patient care.
  • Generative models, particularly conditional generative models (CGMs), offer a solution by generating enhanced synthetic data.

Purpose of the Study:

  • To review the potential of CGMs for creating patient-specific synthetic data in precision medicine.
  • To survey CGM approaches for correcting data representation biases and simulating digital health twins.
  • To explore methods for modeling tabular medical data with CGMs and discuss evaluation criteria.

Main Methods:

  • Literature review of conditional generative model (CGM) approaches.
  • Focus on applications in correcting data representation biases and simulating digital health twins.
  • Analysis of methods for modeling tabular medical data and evaluation criteria.

Main Results:

  • CGMs show significant potential for generating patient-specific synthetic data.
  • Surveyed methods address data representation biases and digital health twin simulations.
  • Exploration of techniques for handling tabular medical data and assessment metrics.

Conclusions:

  • CGMs are a promising tool for enhancing medical datasets and advancing precision medicine.
  • Addressing technical, medical, and ethical challenges is crucial for safe and effective deployment of CGMs.
  • Further research is needed to fully realize the potential of CGMs in healthcare.