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A mobile phone based tool to identify symptoms of common childhood diseases in Ghana: development and evaluation of the integrated clinical algorithm in a cross-sectional study

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)

Mentioned by

twitter
4 tweeters

Citations

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16 Dimensions

Readers on

mendeley
108 Mendeley
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Title
A mobile phone based tool to identify symptoms of common childhood diseases in Ghana: development and evaluation of the integrated clinical algorithm in a cross-sectional study
Published in
BMC Medical Informatics and Decision Making, March 2018
DOI 10.1186/s12911-018-0600-3
Pubmed ID
Authors

Konstantin H. Franke, Ralf Krumkamp, Aliyu Mohammed, Nimako Sarpong, Ellis Owusu-Dabo, Johanna Brinkel, Julius N. Fobil, Axel Bonacic Marinovic, Philip Asihene, Mark Boots, Jürgen May, Benno Kreuels

Abstract

The aim of this study was the development and evaluation of an algorithm-based diagnosis-tool, applicable on mobile phones, to support guardians in providing appropriate care to sick children. The algorithm was developed on the basis of the Integrated Management of Childhood Illness (IMCI) guidelines and evaluated at a hospital in Ghana. Two hundred and thirty-seven guardians applied the tool to assess their child's symptoms. Data recorded by the tool and health records completed by a physician were compared in terms of symptom detection, disease assessment and treatment recommendation. To compare both assessments, Kappa statistics and predictive values were calculated. The tool detected the symptoms of cough, fever, diarrhoea and vomiting with good agreement to the physicians' findings (kappa = 0.64; 0.59; 0.57 and 0.42 respectively). The disease assessment barely coincided with the physicians' findings. The tool's treatment recommendation correlated with the physicians' assessments in 93 out of 237 cases (39.2% agreement, kappa = 0.11), but underestimated a child's condition in only seven cases (3.0%). The algorithm-based tool achieved reliable symptom detection and treatment recommendations were administered conformably to the physicians' assessment. Testing in domestic environment is envisaged.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 108 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 20%
Researcher 11 10%
Student > Bachelor 10 9%
Other 7 6%
Lecturer 7 6%
Other 25 23%
Unknown 26 24%
Readers by discipline Count As %
Medicine and Dentistry 22 20%
Nursing and Health Professions 10 9%
Psychology 6 6%
Engineering 5 5%
Social Sciences 4 4%
Other 23 21%
Unknown 38 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 27 April 2018.
All research outputs
#6,825,216
of 12,861,409 outputs
Outputs from BMC Medical Informatics and Decision Making
#571
of 1,163 outputs
Outputs of similar age
#118,184
of 270,040 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 12,861,409 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,163 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 49th percentile – i.e., 49% 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 270,040 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 55% 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