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Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival
99
Zitationen
8
Autoren
2020
Jahr
Abstract
We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems.
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