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A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

Sunyang FuLiwei WangHuan HeAndrew WenNansu ZongAnamika KumariFeifan LiuSicheng ZhouRui ZhangChenyu LiYanshan WangJennifer St SauverHongfang LiuSunghwan Sohn
Published in: Journal of the American Medical Informatics Association : JAMIA (2024)
The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.
Keyphrases
  • electronic health record
  • machine learning
  • deep learning
  • risk assessment
  • adverse drug