Rationale for the Arden Syntax
Computers and Biomedical Research
Design of a clinical event monitor
Computers and Biomedical Research
Development and evaluation of a computerized admission diagnosis encoding system
Computers and Biomedical Research
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
A temporal constraint structure for extracting temporal information from clinical narrative
Journal of Biomedical Informatics
Terminology model discovery using natural language processing and visualization techniques
Journal of Biomedical Informatics
Computers in Biology and Medicine
Extracting Specific Medical Data Using Semantic Structures
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Journal of Data and Information Quality (JDIQ)
Journal of Biomedical Informatics
Visual summarisation of text for surveillance and situational awareness in hospitals
Proceedings of the 18th Australasian Document Computing Symposium
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Natural language processing (NLP) is critical for improvement of the healthcare process because it can encode clinical data in patient documents. Many clinical applications such as decision support require coded data to function appropriately. However, in order to be applicable for healthcare, performance must be adequate. A valuable automated application is the detection of infectious diseases, such as surveillance of pneumonia in newborns (e.g., neonates) because the disease produces significant rates of morbidity and mortality, and manual surveillance is challenging. Studies have demonstrated that automated surveillance using NLP is a useful adjunct to manual surveillance and an effective tool for infection control practitioners. This paper presents a study evaluating the feasibility of an NLP-based monitoring system to screen for healthcare-associated pneumonia in neonates. We estimated sensitivity, specificity, and positive predictive value by comparing results with clinicians' judgments. Sensitivity was 71% and specificity was 99%. Our results demonstrated that the automated method was feasible.