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May 22, 2026Frontiers in Public HealthOpen Access

Multi-heterogeneous data fusion for enterprise data asset valuation in public health policy context

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Authors

YWYi Weiwei

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Overview

Randomized trial demonstrates improved data valuation accuracy in public health, suggesting enhanced decision support for policy-making.

Key Points

  • This research aims to develop a framework for effectively integrating varied types of data to value enterprise data assets in public health.
  • Developed a framework using FusionNet and an Innovative Fusion Strategy for data integration.
  • Utilized machine learning techniques to fuse heterogeneous data sources.
  • Implemented an Adaptive Data Synthesis mechanism to enhance valuation accuracy.
  • Achieved a 3.6% improvement in valuation accuracy compared to baselines.
  • Enhanced area under the curve (AUC) by 2.2% from baseline measurements.
  • Demonstrated the framework’s potential to support public health policy decisions through improved data valuation.

Cite This Study

Yi Weiwei (2026) studied this question.

synapsesocial.com/papers/6a0ff1dbd674f7c03778b0bbhttps://doi.org/10.3389/fpubh.2026.1756080
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