@inproceedings{64f8b270b011413f8726ca24e83dad6d,
title = "Automatic Generation of Electronic Medical Record Based on GPT2 Model",
abstract = "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.",
keywords = "EMR, GPT2, Text Generation",
author = "Junkun Peng and Pin Ni and Jiayi Zhu and Zhenjin Dai and Yuming Li and Gangmin Li and Xuming Bai",
note = "Funding Information: This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006414",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6180--6182",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
}