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Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study
8
Zitationen
3
Autoren
2023
Jahr
Abstract
BACKGROUND: Negation and speculation unrelated to abnormal findings can lead to false-positive alarms for automatic radiology report highlighting or flagging by laboratory information systems. OBJECTIVE: This internal validation study evaluated the performance of natural language processing methods (NegEx, NegBio, NegBERT, and transformers). METHODS: -scores. In experiment 2, we compared the best model from experiment 1 with 3 established negation and speculation-detection algorithms (NegEx, NegBio, and NegBERT). RESULTS: -score=0.997). CONCLUSIONS: The ALBERT deep learning method showed the best performance. Our results represent a significant advancement in the clinical applications of computer-aided notification systems.
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