Automatic information extraction from childhood cancer pathology reports.

Hong-Jun YoonAlina PelusoEric B DurbinXiao-Cheng WuAntoinette StroupJennifer DohertyStephen SchwartzCharles WigginsLinda CoyleLynne Penberthy

Published in: JAMIA open (2022)

Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.

Keyphrases

childhood cancer
machine learning
young adults
deep learning
adverse drug
endothelial cells
health information
artificial intelligence
working memory
healthcare
big data
induced pluripotent stem cells
emergency department
papillary thyroid
social media
electronic health record