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

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 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.

Materials and methods

A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sourc

SUBMITTER: Gensheimer MF 

PROVIDER: S-EPMC8200275 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature