Information Processing and Management: an International Journal - Special issue on parallel processing and information retrieval
Artificial Intelligence in Medicine
MPLUS: a probabilistic medical language understanding system
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
A temporal constraint structure for extracting temporal information from clinical narrative
Journal of Biomedical Informatics
Distinguishing historical from current problems in clinical reports: which textual features help?
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ONYX: a system for the semantic analysis of clinical text
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Journal of Biomedical Informatics
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Hi-index | 0.00 |
In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams.