A maximum entropy approach to natural language processing
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Selective automated indexing of findings and diagnoses in radiology reports
Computers and Biomedical Research
Feature selection on hierarchy of web documents
Decision Support Systems - Web retrieval and mining
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Modeling Incidental Findings in Radiology Records
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging examinations. In this paper, we present a text processing pipeline to automatically identify clinically important recommendation sentences in radiology reports. Our extraction pipeline is based on natural language processing (NLP) and supervised text classification methods. To develop and test the pipeline, we created a corpus of 800 radiology reports double annotated for recommendation sentences by a radiologist and an internist. We ran several experiments to measure the impact of different feature types and the data imbalance between positive and negative recommendation sentences. Our fully statistical approach achieved the best f-score 0.758 in identifying the critical recommendation sentences in radiology reports.