Cross-document attention-based gated fusion network for automated medical licensing exam

作者：

Highlights：

• A CAMI frame is proposed to tackle the OpenQA medical MRC tasks.

• The proposed CDCA could extract the attentional information across documents.

• The proposed HGFN could dynamically fuse information from multiple documents.

• The proposed ClinicQA is the first public dataset to evaluate clinical diagnosis ability.

• The proposed method greatly outperforms SOTA openQA medical MRC models.

摘要

•A CAMI frame is proposed to tackle the OpenQA medical MRC tasks.•The proposed CDCA could extract the attentional information across documents.•The proposed HGFN could dynamically fuse information from multiple documents.•The proposed ClinicQA is the first public dataset to evaluate clinical diagnosis ability.•The proposed method greatly outperforms SOTA openQA medical MRC models.

论文关键词：Machine reading comprehension,Clinical diagnosis,Multiple document reasoning

论文评审过程：Received 28 July 2021, Revised 11 May 2022, Accepted 11 May 2022, Available online 20 May 2022, Version of Record 31 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117588