Li, Yuming;
Ni, Pin;
Peng, Junkun;
Zhu, Jiayi;
Dai, Zhenjin;
Li, Gangmin;
Bai, Xuming;
(2020)
A Joint Model of Clinical Domain Classification and Slot Filling Based on RCNN and BiGRU-CRF.
In: Baru, Chaitanya and Huan, Jun and Khan, Latifur and Hu, Xiaohua and Ak, Ronay and Tian, Yuanyuan and Barga, Roger and Zaniolo, Carlo and Lee, Kisung and Ye, Yanfang Fanny, (eds.)
2019 IEEE International Conference on Big Data (Big Data).
(pp. pp. 6133-6135).
IEEE: Los Angeles, CA, USA.
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Abstract
The task of the Intent Classification & Slot Filling serves as a key joint task in the voice assistant, which also plays the role of the pre-work in the construction of the medical consultation assistant system. How to distribute a doctor-patient conversation into a formatted electronic medical record to an accurate department (Intent Classification) to extract the key named entities or mentions (Slot Filling) through a specialized domain knowledge recognizer is one of the key steps of the entire system. In real cases, the medical vocabulary and clinical entities in different departments of the hospital often differ to some extent. Therefore, we propose a comprehensive model based on CMed-BERT, RCNN and BiGRU-CRF for a joint task of department identification and slot filling of the specific domain. Experimental results confirmed the competitiveness of our model.
| Type: | Proceedings paper |
|---|---|
| Title: | A Joint Model of Clinical Domain Classification and Slot Filling Based on RCNN and BiGRU-CRF |
| Event: | 2019 IEEE International Conference on Big Data (IEEE BigData 2019) |
| Location: | Los Angeles, CA |
| Dates: | 9 Dec 2019 - 12 Dec 2019 |
| ISBN-13: | 978-1-7281-0858-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BigData47090.2019.9005449 |
| Publisher version: | https://doi.org/10.1109/BigData47090.2019.9005449 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Task analysis; Filling; Electronic medical records; Training; Mathematical model; Medical diagnostic imaging; Semantics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159890 |
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