TY  - GEN
TI  - Automatic Generation of Electronic Medical Record Based on GPT2 Model
T3  - IEEE International Conference on Big Data
CY  - Los Angeles, CA, USA
N2  - Writing Electronic Medical Records (EMR) as one of daily major tasks of doctors, consumes a lot of time and effort from doctors. This paper reports our efforts to generate electronic medical records using the language model. Through the training of massive real-world EMR data, the CMedGPT2 model provided by us can achieve the ideal Chinese electronic medical record generation. The experimental results prove that the generated electronic medical record text can be applied to the auxiliary medical record work to reduce the burden on the compose and provide a fast and accurate reference for composing work.
SP  - 6180
UR  - https://doi.org/10.1109/BigData47090.2019.9006414
A1  - Peng, Junkun
A1  - Ni, Pin
A1  - Zhu, Jiayi
A1  - Dai, Zhenjin
A1  - Li, Yuming
A1  - Li, Gangmin
A1  - Bai, Xuming
Y1  - 2019/01/01/
PB  - IEEE
SN  - 2639-1589
KW  - Liver; Training; Cancer; Electronic medical records; Data models; Hospitals; Task analysis
AV  - public
ID  - discovery10159896
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
EP  - 6182
ER  -