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Predicting Colorectal Cancer Mortality: Models to Facilitate Patient‐Physician Conversations and Inform Operational Decision Making
Author(s) -
Bjarnadottir Margret,
Anderson David,
Zia Leila,
Rhoads Kim
Publication year - 2018
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12896
Subject(s) - predictive power , colorectal cancer , term (time) , clinical decision making , medicine , computer science , cancer , intensive care medicine , philosophy , physics , epistemology , quantum mechanics
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.