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A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2018
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3 tweeters

Citations

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13 Dimensions

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Title
A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts
Published in
BMC Medical Informatics and Decision Making, March 2018
DOI 10.1186/s12911-018-0595-9
Pubmed ID
Authors

Tianyong Hao, Xiaoyi Pan, Zhiying Gu, Yingying Qu, Heng Weng

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 30%
Researcher 3 9%
Student > Ph. D. Student 3 9%
Other 2 6%
Student > Doctoral Student 2 6%
Other 2 6%
Unknown 11 33%
Readers by discipline Count As %
Computer Science 12 36%
Medicine and Dentistry 3 9%
Psychology 2 6%
Linguistics 1 3%
Environmental Science 1 3%
Other 2 6%
Unknown 12 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 June 2019.
All research outputs
#9,396,357
of 15,348,475 outputs
Outputs from BMC Medical Informatics and Decision Making
#906
of 1,391 outputs
Outputs of similar age
#157,574
of 280,012 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 4 outputs
Altmetric has tracked 15,348,475 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,391 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.