Tianyong Hao1,2, Xiaoyi Pan1, Zhiying Gu1, Yingying Qu3, Heng Weng4. 1. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China. 2. School of Computer, South China Normal University, Guangzhou, China. 3. School of Business, Guangdong University of Foreign Studies, Guangzhou, China. jessie.qu@gdufs.edu.cn. 4. The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. ww128@qq.com.
Abstract
BACKGROUND: Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts. METHODS: A novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple <M, A, N>, TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N. RESULTS: Based on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries. CONCLUSIONS: An automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.
BACKGROUND: Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts. METHODS: A novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple <M, A, N>, TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N. RESULTS: Based on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries. CONCLUSIONS: An automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.
Authors: Sunghwan Sohn; Kavishwar B Wagholikar; Dingcheng Li; Siddhartha R Jonnalagadda; Cui Tao; Ravikumar Komandur Elayavilli; Hongfang Liu Journal: J Am Med Inform Assoc Date: 2013-04-04 Impact factor: 4.497
Authors: Gregory W Hruby; Mary Regina Boland; James J Cimino; Junfeng Gao; Adam B Wilcox; Julia Hirschberg; Chunhua Weng Journal: AMIA Jt Summits Transl Sci Proc Date: 2013-03-18