Open AccessAutomated model versus treating physician for predicting survival time of patients with metastatic cancer
Author(s) -
Michael F. Gensheimer,
S. Aggarwal,
Kathryn R.K. Benson,
Justin N. Carter,
Suzanne Bakken Henry,
D. L. Wood,
Scott G. Soltys,
Steven Hancock,
Erqi L. Pollom,
Nigam H. Shah,
Daniel T. Chang
Publication year - 2020
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa290
Subject(s) - life expectancy , medicine , expectancy theory , machine learning , radiation therapy , cancer , artificial intelligence , radiation oncologist , medical record , survival analysis , computer science , population , psychology , social psychology , environmental health
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 machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.