On 95,008 pediatric chest X-ray reports, six NLP labeling systems varied widely in entity extraction and assertion classification, with consensus-based accuracy between 50% and 76%.
Journal of biomedical informatics, 2018
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Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset
On 95,008 pediatric chest X-ray reports, six NLP labeling systems varied widely in entity extraction and assertion classification, with consensus-based accuracy between 50% and 76%.