Poster
in
Affinity Workshop: Tiny Papers Poster Session 2
Transfer Learning for Global Feature Importance Measurements
Xin Li · Siqi Li · Qiming Wu · Kunyu Yu
Halle B #345
Abstract:
Understanding feature importance is crucial for conducting interpretable clinical decision-making. However, the reliability of such analyses can be heavily impacted by the available sample size, placing sites with lower data quality and smaller sample sizes at inherent disadvantages. To address the challenge, we propose a model-agnostic transfer learning-based approach for feature importance measurement and evaluate its effectiveness using real-world heterogeneous electronic health records.
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