Automated model versus treating physician for predicting survival time of patients with metastatic cancer

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 28; no. 6; pp. 1108 - 1116
• Main Authors: Gensheimer, Michael F, Aggarwal, Sonya, Benson, Kathryn R.K, Carter, Justin N, Henry, A. Solomon, Wood, Douglas J, Soltys, Scott G, Hancock, Steven, Pollom, Erqi, Shah, Nigam H, Chang, Daniel T
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 12.06.2021
• Subjects: Aged; Area Under Curve; Electronic Health Records; Female; Humans; Kaplan-Meier Estimate; Life Expectancy; Machine Learning; Male; Middle Aged; Neoplasm Metastasis - radiotherapy; Neoplasms - mortality; Prognosis; Radiation Oncologists; Research and Applications; ROC Curve; radiotherapy; natural language processing; neoplasms; machine learning; prognosis

Abstract Objective Being able to predict a patient’s life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a mac...