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Automatic infection detection based on electronic medical records

Overview of attention for article published in BMC Bioinformatics, April 2018
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  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
2 tweeters

Citations

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18 Dimensions

Readers on

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46 Mendeley
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Title
Automatic infection detection based on electronic medical records
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2101-x
Pubmed ID
Authors

Huaixiao Tou, Lu Yao, Zhongyu Wei, Xiahai Zhuang, Bo Zhang

Abstract

Making accurate patient care decision, as early as possible, is a constant challenge, especially for physicians in the emergency department. The increasing volumes of electronic medical records (EMRs) open new horizons for automatic diagnosis. In this paper, we propose to use machine learning approaches for automatic infection detection based on EMRs. Five categories of information are utilized for prediction, including personal information, admission note, vital signs, diagnose test results and medical image diagnose. Experimental results on a newly constructed EMRs dataset from emergency department show that machine learning models can achieve a decent performance for infection detection with area under the receiver operator characteristic curve (AUC) of 0.88. Out of all the five types of information, admission note in text form makes the most contribution with the AUC of 0.87. This study provides a state-of-the-art EMRs processing system to automatically make medical decisions. It extracts five types of features associated with infection and achieves a decent performance on automatic infection detection based on machine learning models.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 17%
Researcher 7 15%
Student > Bachelor 6 13%
Other 5 11%
Student > Postgraduate 4 9%
Other 6 13%
Unknown 10 22%
Readers by discipline Count As %
Computer Science 18 39%
Medicine and Dentistry 12 26%
Biochemistry, Genetics and Molecular Biology 2 4%
Social Sciences 2 4%
Agricultural and Biological Sciences 1 2%
Other 1 2%
Unknown 10 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 August 2018.
All research outputs
#10,548,602
of 16,476,438 outputs
Outputs from BMC Bioinformatics
#4,168
of 5,939 outputs
Outputs of similar age
#170,103
of 282,208 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 24 outputs
Altmetric has tracked 16,476,438 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,939 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 282,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.