Supervised machine learning and active learning in classification of radiology reports.

ABSTRACT:

Objective

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.

Materials and methods

In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW C

SUBMITTER: Nguyen DH 

PROVIDER: S-EPMC4147614 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature