Natural Language Processing to identify pneumonia from radiology reports.

Purpose: This study aimed to develop Natural Language Processing (NLP) approaches to supplement manual outcome validation, specifically to validate pneumonia cases from chest radiograph reports.

Methods: We trained one NLP system, ONYX, using radiograph reports from children and adults that were previously manually reviewed. We then assessed its validity on a test set of 5000 reports.

Methodology to develop and evaluate a semantic representation for NLP.

Natural language processing applications that extract information from text rely on semantic representations. The objective of this paper is to describe a methodology for creating a semantic representation for information that will be automatically extracted from textual clinical records. We illustrate two of the four steps of the methodology in this paper using the case study of encoding information from dictated dental exams: (1) develop an initial representation from a set of training documents and (2) iteratively evaluate and evolve the representation while developing annotation guidelines.

Identification of trauma patients at a level 1 trauma center utilizing natural language processing.

Trauma centers use trauma registries to collect information on injured patients they receive. The information is used for evaluation of care rendered, research, system and process improvement, and evaluation of injury prevention programs. Identification of patients qualifying for inclusion in registries can be problematic.

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

Objective: Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance.

Introduction: Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form.