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Bridging a translational gap: using machine learning to improve the prediction of PTSD

Overview of attention for article published in BMC Psychiatry, March 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
twitter
11 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
94 Dimensions

Readers on

mendeley
159 Mendeley
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Title
Bridging a translational gap: using machine learning to improve the prediction of PTSD
Published in
BMC Psychiatry, March 2015
DOI 10.1186/s12888-015-0399-8
Pubmed ID
Authors

Karen-Inge Karstoft, Isaac R Galatzer-Levy, Alexander Statnikov, Zhiguo Li, Arieh Y Shalev

Abstract

Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 2 1%
United States 1 <1%
Netherlands 1 <1%
France 1 <1%
Unknown 154 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 23%
Student > Master 20 13%
Student > Bachelor 18 11%
Researcher 17 11%
Student > Doctoral Student 12 8%
Other 31 19%
Unknown 24 15%
Readers by discipline Count As %
Psychology 39 25%
Medicine and Dentistry 25 16%
Computer Science 18 11%
Neuroscience 8 5%
Agricultural and Biological Sciences 6 4%
Other 27 17%
Unknown 36 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 June 2016.
All research outputs
#647,047
of 15,049,914 outputs
Outputs from BMC Psychiatry
#198
of 3,353 outputs
Outputs of similar age
#12,407
of 219,009 outputs
Outputs of similar age from BMC Psychiatry
#1
of 1 outputs
Altmetric has tracked 15,049,914 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,353 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has done particularly well, scoring higher than 94% 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 219,009 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% 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