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Using Machine Learning to Predict Survival Following Prostatectomy

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Year:: 2019
Contributor:: Kim Jr., Isaac (Author); Jung, Eric (Author); Pareek, Gyan (Author); Aluthge, Dilum (Author); Chen, Elizabeth (Author); Sarkar, Indra Neil (Author); Brown University. Alpert Medical School. Scholarly Concentration Program. Biomedical Informatics (research program)
Subject:: Machine learning; Prostate--Cancer; Prostatectomy
Language:: English

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Abstract:: Tissue 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.
Notes:: Scholarly concentration: Biomedical Informatics

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Kim Jr., Isaac, Jung, Eric, Pareek, Gyan, et al., "Using Machine Learning to Predict Survival Following Prostatectomy" (2019). Warren Alpert Medical School Academic Symposium. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:957203/

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Warren Alpert Medical School Academic Symposium

The Warren Alpert Medical School Academic Symposium is an annual event at Warren Alpert Medical School of Brown University that provides Year II medical students a venue to present their summer research in a poster format. Participation in the Symposium …
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