Isaac E. Kim

Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

OBJECTIVE To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models. METHODS Using the Surveillance, Epidemiology, and End Results (SEER) 17 Database, w...