Predicting Colorectal Cancer Mortality: Models to Facilitate Patient-Physician Conversations and Inform Operational Decision Making

• 成果类型: Article
• 署名作者: Bjarnadottir, Margret; Anderson, David; Zia, Leila; Rhoads, Kim
• 署名单位: University System of Maryland; University of Maryland College Park; City University of New York (CUNY) System; Baruch College (CUNY); Wikimedia Foundation; Stanford University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12896
• 发表日期: 2018
• 页码: 2162-2183
• 关键词: data mining; medical decision-making; survival analysis; personalized medicine

摘要

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.