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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 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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2 news outlets
blogs
1 blog
twitter
10 X users
facebook
2 Facebook pages

Citations

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129 Dimensions

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mendeley
191 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, For members of the Jerusalem Trauma Outreach and Prevention Study (J-TOPS) group

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.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 191 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 186 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 21%
Student > Master 21 11%
Researcher 20 10%
Student > Bachelor 20 10%
Student > Doctoral Student 12 6%
Other 40 21%
Unknown 37 19%
Readers by discipline Count As %
Psychology 43 23%
Medicine and Dentistry 25 13%
Computer Science 21 11%
Neuroscience 8 4%
Unspecified 7 4%
Other 36 19%
Unknown 51 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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
#1,276,441
of 24,942,536 outputs
Outputs from BMC Psychiatry
#393
of 5,293 outputs
Outputs of similar age
#16,193
of 267,616 outputs
Outputs of similar age from BMC Psychiatry
#9
of 86 outputs
Altmetric has tracked 24,942,536 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,293 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done particularly well, scoring higher than 92% 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 267,616 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 93% of its contemporaries.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.