Predicting Colorectal Cancer Mortality: Models to Facilitate Patient‐Physician Conversations and Inform Operational Decision Making. Issue 12 (3rd July 2018)

• Record Type: Journal Article
• Authors: Bjarnadottir, Margret; Anderson, David; Zia, Leila; Rhoads, Kim
• Other Names: Bretthauer Kurt M. guestEditor.; Savin Sergei guestEditor.
• Is Part Of: Production and operations management. Volume 27:Issue 12(2018)
• Page Start: 2162
• Page End: 2183
• Publication Date: 2018-07-03
• Subjects: data mining -- medical decision‐making -- survival analysis -- personalized medicine; Production management -- Periodicals; 658.505
• DOI: 10.1111/poms.12896
• Languages: English
• ISSNs: 1059-1478

Abstract

Abstract : Having accurate, unbiased prognosis information can help patients and providers make better decisions about what course of treatment to take. Using a comprehensive dataset of all colorectal cancer patients in California, we generate predictive models that estimate short‐term and medium‐term survival probabilities for patients based on their clinical and demographic information. Our study addresses some of the contradictions in the literature about survival rates and significantly improves predictive power over the performance of any model in previously published studies.