Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports.

ABSTRACT:

Background

Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters.

Objectives

The purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their "temporality".

Methods

The measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground

SUBMITTER: Sevenster M 

PROVIDER: S-EPMC4586346 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature