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Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

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

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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7 tweeters

Citations

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10 Dimensions

Readers on

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54 Mendeley
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Title
Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing
Published in
BMC Medical Informatics and Decision Making, May 2018
DOI 10.1186/s12911-018-0617-7
Pubmed ID
Authors

Qiu-Yue Zhong, Elizabeth W. Karlson, Bizu Gelaye, Sean Finan, Paul Avillach, Jordan W. Smoller, Tianxi Cai, Michelle A. Williams

Abstract

We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs). Women aged 10-64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our "datamart." Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women. Using diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review. The use of NLP substantially improves the sensitivity of screening suicidal behavior in EMRs. However, the prevalence of confirmed suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP. NLP should be used together with diagnostic codes for future EMR-based phenotyping studies for suicidal behavior.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 20%
Researcher 10 19%
Student > Postgraduate 4 7%
Other 3 6%
Student > Ph. D. Student 3 6%
Other 6 11%
Unknown 17 31%
Readers by discipline Count As %
Medicine and Dentistry 13 24%
Psychology 6 11%
Social Sciences 4 7%
Computer Science 4 7%
Engineering 3 6%
Other 6 11%
Unknown 18 33%

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 27 September 2018.
All research outputs
#3,844,899
of 13,810,416 outputs
Outputs from BMC Medical Informatics and Decision Making
#448
of 1,243 outputs
Outputs of similar age
#96,570
of 275,663 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 9 outputs
Altmetric has tracked 13,810,416 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,243 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 63% 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 275,663 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 64% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.