Automated outcome classification of emergency department computed tomography imaging reports.

ABSTRACT:

Background

Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes.

Objectives

The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction.

Methods

SUBMITTER: Yadav K 

PROVIDER: S-EPMC3898888 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature