Abstract
NLP based technical approach has shown promise in leveraging data from colonoscopy reports for adenoma detection rates (ADRs) with a greater than 90% accuracy. Though extremely useful, NLP alone is limited by its inability to recognize text contained in imaging format such as scanned documents. Optical character recognition (OCR) is a technology that enables conversion of scanned paper documents into editable data which can then be accessed by NLP. The aim of this study is to validate a novel hybrid OCR/NLP approach in accurately extracting relevant clinical information from scanned colonoscopy reports.
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