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Weight-Based Framework for Predictive Modeling of Multiple Databases With Noniterative Communication Without Data

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  • 1Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

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Summary

This study introduces a privacy-preserving weight-based model for multi-institutional data, achieving comparable predictive performance to centralized models without data sharing. The method enhances generalizability for biomedical research.

Keywords:
data sharingdistributed datamulti-institutional studyprivacy-protecting methods

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Area of Science:

  • Biomedical research
  • Data science
  • Health informatics

Background:

  • Ensuring representative study populations is vital for generalizability in biomedical research.
  • Multi-institutional data offers advantages but poses privacy challenges due to sensitive patient information.
  • A privacy-preserving methodological approach is needed for utilizing multi-institution medical data without direct data sharing.

Purpose of the Study:

  • To develop a weight-based integrated predictive model using multi-institutional data.
  • To improve predictive performance and generalizability without iterative communication or patient-level data sharing.
  • To enable privacy-preserving distributed research across institutions.

Main Methods:

  • Developed a weight-based integrated model assigning weights to individual institutional models.
  • Conducted simulations to analyze weight characteristics and determine optimal repetition counts.
  • Validated the model using real multi-institutional data from 10 hospitals (2845 ICU stays) to predict ICU mortality.
  • Compared the model's performance against a centralized model using proportional overlap of confidence intervals for Area Under the Curve (AUC) and odds ratios.

Main Results:

  • Simulations revealed institutional weights depend on data size and model fit, requiring 200 weight repetitions per institution for stability.
  • The weight-based integrated model achieved an AUC of 81.95% (95% CI: 80.03%-83.87%), comparable to the centralized model's 81.36% (95% CI: 79.37%-83.36%).
  • Proportional overlap of AUC confidence intervals was ~1.70, and overlap for 10 of 11 odds ratios exceeded 1, indicating high similarity between models.

Conclusions:

  • The weight-based integrated model demonstrated performance similar to the centralized model using real multi-institutional data, without requiring data sharing or iterative communication.
  • This approach effectively integrates potentially overfitted or underfitted institutional models into a balanced, generalizable predictive tool.
  • The proposed method offers an efficient distributed research strategy, enhancing model generalizability and facilitating collaborative studies while preserving data privacy.