ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
A text processing pipeline to extract recommendations from radiology reports
Journal of Biomedical Informatics
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Information loss can occur between radiologists and patients with regard to incidental findings (unexpected or uncertain results) in the interpretation of an image. When a healthcare provider fails to inform a patient of a potential medical issue, quality of care is decreased and medical-legal issues arise. We discuss issues in modeling incidental findings in clinical records, examine available machine learning inputs, and propose a clinical text analysis system using weighted syntactic matching and user feedback learning. To demonstrate that our proposal would support better quality of care at lower cost than prior process-based solutions, we evaluate a prototype system on a gold-standard set of 580 records, yielding 82% sensitivity and 92% specificity, as compared with 43% sensitivity and 100% specificity for an existing manual review process.