Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival
98
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.
Ähnliche Arbeiten
Docetaxel plus Prednisone or Mitoxantrone plus Prednisone for Advanced Prostate Cancer
2004 · 5.692 Zit.
Decision Curve Analysis: A Novel Method for Evaluating Prediction Models
2006 · 5.057 Zit.
Increased Survival with Enzalutamide in Prostate Cancer after Chemotherapy
2012 · 4.529 Zit.
Biochemical Outcome After Radical Prostatectomy, External Beam Radiation Therapy, or Interstitial Radiation Therapy for Clinically Localized Prostate Cancer
1998 · 4.484 Zit.
Screening and Prostate-Cancer Mortality in a Randomized European Study
2009 · 3.988 Zit.