Abstract
The widespread adoption of electronic health records has resulted in an abundance
of imaging and clinical information. New data-processing technologies have the potential
to revolutionize the practice of medicine by deriving clinically meaningful insights
from large-volume data. Among those techniques is supervised machine learning, the
study of computer algorithms that use self-improving models that learn from labeled
data to solve problems. One clinical area of application for supervised machine learning
is within oncology, where machine learning has been used for cancer diagnosis, staging,
and prognostication. This review describes a framework to aid clinicians in understanding
and critically evaluating studies applying supervised machine learning methods. Additionally,
we describe current studies applying supervised machine learning techniques to the
diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological
cancers and other related pathologies.
Keywords
machine learning - supervised learning - automated diagnosis - artificial intelligence
