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Online Transfer Learning for RSV Case Detection.

Yiming Sun¹, Yuhe Gao², Runxue Bao³

¹Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh.

Proceedings. IEEE International Conference on Healthcare Informatics

|May 20, 2025

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This summary is machine-generated.

This study introduces Multi-Source Adaptive Weighting (MSAW), an online transfer learning method that dynamically adjusts weights for sequential data classification. MSAW improves performance in healthcare applications by integrating historical and new data effectively.

Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

Transfer learning is crucial for machine learning but struggles with sequential data due to limited labels.
Existing methods often fail to optimally integrate historical data with newly acquired information.

Purpose of the Study:

To introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method.
To address the challenge of limited class labels in sequential data classification tasks.
To dynamically integrate historical knowledge with incrementally accumulated new data.

Main Methods:

Developed MSAW, an online multi-source transfer learning method.
Integrated a dynamic weighting mechanism into an ensemble framework.

Keywords:

Respiratory Syncytial Virus case detection dynamic weighting mechanism electronic health record ensemble method online transfer learning

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Applied MSAW to detect Respiratory Syncytial Virus cases using electronic health records.

Main Results:

MSAW demonstrated performance improvements over various baselines, including static weighting and online learning refinement.
The method effectively integrated historical knowledge with progressively accumulated new data.
Showcased the capacity of MSAW to adapt to evolving data situations.

Conclusions:

MSAW offers an effective solution for transfer learning with sequential data and limited labels.
Online transfer learning shows significant potential in healthcare for adaptive machine learning models.
MSAW enables dynamic adaptation of models to evolving healthcare data scenarios.