Named Entity Recognition for Chinese Electronic Medical Records Based on Multitask and Transfer Learning

Current work on named entities for Chinese electronic medical records requires training a separate model for each different type of electronic medical record, the performance of which depends on the amount of training data available for each dataset. However, different types of electronic medical re...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.77375-77382
Main Authors: Guo, Wenming, Lu, Junda, Han, Fang
Format: Article
Language:English
Subjects:
BERT
Bit error rate
Coders
Datasets
Decoding
Electronic health records
Electronic medical records
Learning
multitask learning
Multitasking
Named entity recognition
Task analysis
Training
Transfer learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Current work on named entities for Chinese electronic medical records requires training a separate model for each different type of electronic medical record, the performance of which depends on the amount of training data available for each dataset. However, different types of electronic medical records share similar semantic information with each other, while current models do not take full advantage of this potentially common knowledge. To overcome the mentioned problem, we propose a multi-task learning framework to transfer multiple types of electronic medical records through a shared encoder. Experiments demonstrate that our model achieves substantially better performance compared with the single-task model based on BERT. F1 scores improved by more than 1% on average across the four datasets, with individual datasets improving precision by more than 3.5%. Further analysis shows that our model still achieves better F1 scores on long tail datasets and small size datasets.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3192866