Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on Machine Learning

DOI

10.2991/icmemtc-16.2016.290How to use a DOI?

Keywords

Chinese Electronic Medical Records; Information extraction; Named Entity Recognition; assertion classification; Machine Learning

Abstract

With the rapid growth of electronic medical records (EMRs) in China, large amounts of clinical data have been accumulated. However, limited work for extracting information from EMRs in Chinese has been conducted. In this work, using manually annotated dataset of EMRs in Chinese, we investigated the clinical Named Entities Recognition (NER) based on Conditional Random Field (CRF) and further built a Support Vector Machine (SVM) classifier to determine their assertion status and evaluate the contributions of different features for assertion classification. For Chinese clinical NER, our CRF-based classifier achieved the best F-measure of 89.07%, while the SVM-based assertion classifier achieved a maximum F-measure of 94.10%. Our work suggests that machine learning methods are helpful in NER and assertion determination for Chinese medical clinical records.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianhong Wang
AU  - Yousong Peng
AU  - Bin Liu
AU  - Zhiqiang Wu
AU  - Lizong Deng
AU  - Taijiao Jiang
PY  - 2016/04
DA  - 2016/04
TI  - Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on Machine Learning
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 1503
EP  - 1508
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.290
DO  - 10.2991/icmemtc-16.2016.290
ID  - Wang2016/04
ER  -