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Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2015
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  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

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6 tweeters

Citations

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24 Dimensions

Readers on

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67 Mendeley
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1 CiteULike
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Title
Using text mining techniques to extract phenotypic information from the PhenoCHF corpus
Published in
BMC Medical Informatics and Decision Making, June 2015
DOI 10.1186/1472-6947-15-s2-s3
Pubmed ID
Authors

Noha Alnazzawi, Paul Thompson, Riza Batista-Navarro, Sophia Ananiadou

Abstract

Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 3 4%
United States 3 4%
Netherlands 1 1%
Germany 1 1%
Unknown 59 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 31%
Student > Ph. D. Student 15 22%
Student > Master 8 12%
Student > Bachelor 3 4%
Professor 3 4%
Other 4 6%
Unknown 13 19%
Readers by discipline Count As %
Computer Science 20 30%
Medicine and Dentistry 7 10%
Agricultural and Biological Sciences 6 9%
Engineering 4 6%
Psychology 3 4%
Other 10 15%
Unknown 17 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 September 2015.
All research outputs
#5,718,859
of 19,510,965 outputs
Outputs from BMC Medical Informatics and Decision Making
#596
of 1,738 outputs
Outputs of similar age
#57,514
of 201,263 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 19,510,965 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,738 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 65% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 201,263 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them