Mining medical related temporal information from patients’ self-description

International Journal of Crowd Science

Lichao Zhu¹ 

关键词: Support vector machine;    Co-reference;    Conditional random field;    Temporal information extraction;    Word embedding;   

DOI  :  10.1108/IJCS-08-2017-0018

学科分类：工程和技术（综合）

来源: Emerald Publishing

【 摘 要 】

Purpose The purpose of this paper is to develop a new method to extract medical temporal information from online health communities. Design/methodology/approach The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words. Findings For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult t...

【 授权许可】

CC BY