Medical Name Entity Recognition and Application in Chinese Admission Record of Stroke Patients Based on CRF and RUTA rule
- VernacularTitle:基于CRF与RUTA规则相结合的卒中入院记录医学实体识别及应用
- Author:
Yuan XU
1
;
Yan-Qiu GE
;
Qiang WANG
;
Gang XIONG
;
Ying-Ping YI
Author Information
1. 南昌大学附属第二医院临床医疗大数据研究中心
- Keywords:
Chinese medical record;
named entity recognition;
CRF;
stroke
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2018;39(3):455-462
- CountryChina
- Language:Chinese
-
Abstract:
[Objective] To research the construction and optimization of natural language processing model for unstructured medical records,and using the model to extract structured data from medical records of stroke patients in Jiangxi Medical Big Data Platform.[Methods] According to the actual needs of clinical research,a stroke specialist entity annotation system and named entity annotation corpus were constructed based on 500 hospital admission records of stroke patients,which randomly selected between 2011 to 2016 from the Jiangxi provincial medical big data platform.The corpus is used to construct a named entity extraction model based on CRF and RUTA rules,and the recognition accuracy is improved by adjusting RUTA rules and parameters.[Results] Accuracy rate of extraction model was 0.960,recall rate was 0.916 and F-score was 0.939.The extraction model was used to extract 264 580 entities and 1 161 077 entity relation from 10 295 stroke patients' admission records of the medical big data platform.[Conclusions] The constructed natural language extraction model has a high recognition accuracy,which can accurately obtain valuable scientific research data of patients' past history,life history and clinical manifestations from a large number of unstructured medical records and effectively improve the clinical research efficiency and scientific research level of cerebrovascular diseases.
