Using Machine Learning to Predict Survival Following ProstatectomyTissue biopsy and the Gleason scale remain the standard care to predict the aggressiveness of prostate cancer. Recent studies have shown that other clinical factors including comorbidities and genes may play significant roles in determining overall survival. 1 Existing nomograms including Memorial Sloan Kettering Cancer Center’s (MSKCC) prostate cancer nomograms do not incorporate comorbidities when predicting survival. Leveraging machine learning, this study aimed to use comorbidities to predict survival for prostate cancer patients following radical prostatectomy.Kim Jr., IsaacAuthorJung, EricAuthorPareek, GyanAuthorAluthge, DilumAuthorChen, ElizabethAuthorSarkar, Indra NeilAuthorBrown University. Alpert Medical School. Scholarly Concentration Program. Biomedical Informaticsresearch programMachine learningProstate--CancerProstatectomyEnglish2019Biomedical InformaticsAll rights reservedtext