Extracting important information from Chinese Operation Notes with natural language processing methods

Hui Wang; Weide Zhang; Qiang Zeng; Zuofeng Li; Kaiyan Feng; Lei Liu

doi:10.1016/j.jbi.2013.12.017

Extracting important information from Chinese Operation Notes with natural language processing methods

J Biomed Inform. 2014 Apr:48:130-6. doi: 10.1016/j.jbi.2013.12.017. Epub 2014 Jan 31.

Authors

Hui Wang¹, Weide Zhang², Qiang Zeng³, Zuofeng Li³, Kaiyan Feng⁴, Lei Liu⁵

Affiliations

¹ Shanghai Public Health Clinical Center, Institutes of Biomedical Sciences, and Key laboratory of Medical Molecular Virology, Ministry of Education and Health, Fudan University, Shanghai, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, Shanghai, China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
² Zhongshan Hospital, Fudan University, Shanghai, China.
³ Shanghai Center for Bioinformatics Technology, Shanghai, China.
⁴ BGI-Shenzhen, Shenzhen, China.
⁵ Shanghai Public Health Clinical Center, Institutes of Biomedical Sciences, and Key laboratory of Medical Molecular Virology, Ministry of Education and Health, Fudan University, Shanghai, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, Shanghai, China; Shanghai Center for Bioinformatics Technology, Shanghai, China. Electronic address: liulei@fudan.edu.cn.

PMID: 24486562
DOI: 10.1016/j.jbi.2013.12.017

Abstract

Extracting information from unstructured clinical narratives is valuable for many clinical applications. Although natural Language Processing (NLP) methods have been profoundly studied in electronic medical records (EMR), few studies have explored NLP in extracting information from Chinese clinical narratives. In this study, we report the development and evaluation of extracting tumor-related information from operation notes of hepatic carcinomas which were written in Chinese. Using 86 operation notes manually annotated by physicians as the training set, we explored both rule-based and supervised machine-learning approaches. Evaluating on unseen 29 operation notes, our best approach yielded 69.6% in precision, 58.3% in recall and 63.5% F-score.

Keywords: Chinese EMR; Clinical operation notes; Conditional random fields; Information extraction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence*
Carcinoma / diagnosis*
Carcinoma / pathology
China
Computer Simulation
Computer Systems
Data Mining / methods
Electronic Health Records
Humans
Language
Liver Neoplasms / diagnosis*
Liver Neoplasms / pathology
Medical Informatics / methods
Natural Language Processing*
Software