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Prospects for automated diagnosis of verbal autopsies

Overview of attention for article published in BMC Medicine, February 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 blog
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1 X user

Citations

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13 Dimensions

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44 Mendeley
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1 CiteULike
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Title
Prospects for automated diagnosis of verbal autopsies
Published in
BMC Medicine, February 2014
DOI 10.1186/1741-7015-12-18
Pubmed ID
Authors

Michel Garenne

Abstract

Verbal autopsy is a method for assessing probable causes of death from lay reporting of signs, symptoms and circumstances by family members or caregivers of a deceased person. Several methods of automated diagnoses of causes of death from standardized verbal autopsy questionnaires have been developed recently (Inter-VA, Tariff, Random Forest and King-Lu). Their performances have been assessed in a series of papers in BMC Medicine. Overall, and despite high specificity, the current strategies of automated computer diagnoses lead to relatively low sensitivity and positive predictive values, even for causes which are expected to be easily assessed by interview. Some methods have even abnormally low sensitivity for selected diseases of public health importance and could probably be improved. Ways to improve the current strategies are proposed: more detailed questionnaires; using more information on disease duration; stratifying for large groups of causes of death by age, sex and main category; using clusters of signs and symptoms rather than quantitative scores or ranking; separating indeterminate causes; imputing unknown cause with appropriate methods. Please see related articles: http://www.biomedcentral.com/1741-7015/12/5; http://www.biomedcentral.com/1741-7015/12/19; http://www.biomedcentral.com/1741-7015/12/20; http://www.biomedcentral.com/1741-7015/12/21; http://www.biomedcentral.com/1741-7015/12/22; http://www.biomedcentral.com/1741-7015/12/23.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 18%
Researcher 6 14%
Student > Ph. D. Student 5 11%
Student > Bachelor 4 9%
Student > Doctoral Student 3 7%
Other 11 25%
Unknown 7 16%
Readers by discipline Count As %
Medicine and Dentistry 18 41%
Social Sciences 6 14%
Nursing and Health Professions 3 7%
Computer Science 3 7%
Agricultural and Biological Sciences 2 5%
Other 4 9%
Unknown 8 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 04 February 2014.
All research outputs
#4,093,326
of 22,743,667 outputs
Outputs from BMC Medicine
#2,026
of 3,413 outputs
Outputs of similar age
#49,852
of 307,189 outputs
Outputs of similar age from BMC Medicine
#31
of 59 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,413 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one is in the 40th percentile – i.e., 40% 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 307,189 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.