Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers
Published in BMC Medical Informatics and Decision Making, 2021
It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports.
Recommended citation: Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Soichiro Miki, Takeyuki Watadani, Takeharu Yoshikawa, Naoto Hayashi, Osamu Abe. Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers. BMC Med Inform Decis Mak 2021;21:262.
Recommended citation: Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Soichiro Miki, Takeyuki Watadani, Takeharu Yoshikawa, Naoto Hayashi, Osamu Abe. Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers. BMC Med Inform Decis Mak 2021;21:262. https://link.springer.com/article/10.1186/s12911-021-01623-6