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Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

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

Hui Wang¹, Weide Zhang², Qiang Zeng³

¹Shanghai 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.

Journal of Biomedical Informatics

|February 4, 2014

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This study developed methods for extracting tumor information from Chinese clinical notes on liver cancer operations. The best approach achieved 63.5% F-score, improving information extraction for medical research.

Area of Science:

Clinical Informatics
Natural Language Processing (NLP)
Oncology

Background:

Extracting information from unstructured clinical narratives is crucial for various medical applications.
While NLP in electronic medical records is well-researched, its application to Chinese clinical narratives remains underexplored.
Hepatic carcinoma operation notes contain valuable tumor-related data that is challenging to extract.

Purpose of the Study:

To develop and evaluate NLP methods for extracting tumor-related information from Chinese operation notes of hepatic carcinomas.
To compare rule-based and supervised machine-learning approaches for this specific task.
To assess the performance of the developed methods on unseen clinical data.

Main Methods:

Utilized a training set of 86 manually annotated Chinese operation notes for hepatic carcinomas.

Keywords:

Chinese EMR Clinical operation notes Conditional random fields Information extraction

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Last Updated: May 3, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

Explored both rule-based and supervised machine-learning techniques for information extraction.

Evaluated the best performing approach on an independent test set of 29 operation notes.

Main Results:

The best NLP approach achieved a precision of 69.6% for extracting tumor-related information.
Recall for the best approach was 58.3%, indicating a significant portion of relevant information was captured.
An overall F-score of 63.5% was obtained, demonstrating the effectiveness of the developed method.

Conclusions:

The study successfully developed and evaluated NLP methods for extracting tumor information from Chinese hepatic carcinoma operation notes.
The findings highlight the potential of NLP to unlock valuable data within unstructured Chinese clinical narratives.
Further research can build upon these results to enhance clinical data analysis and applications in oncology.