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Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)
by
Ribeiro, J. D.
, Fox, K. R.
, Kleiman, E. M.
, Chang, B. P.
, Bentley, K. H.
, Franklin, J. C.
, Nock, M. K.
in
Algorithms
/ Anxiety
/ Artificial intelligence
/ Classification
/ Clinical psychology
/ Emotional disorders
/ Humans
/ Learning
/ Learning algorithms
/ Machine Learning
/ Meta-analysis
/ Psychiatry
/ Risk factors
/ Search engines
/ Self destructive behavior
/ Statistical methods
/ Suicide
/ Suicides & suicide attempts
2016
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Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)
by
Ribeiro, J. D.
, Fox, K. R.
, Kleiman, E. M.
, Chang, B. P.
, Bentley, K. H.
, Franklin, J. C.
, Nock, M. K.
in
Algorithms
/ Anxiety
/ Artificial intelligence
/ Classification
/ Clinical psychology
/ Emotional disorders
/ Humans
/ Learning
/ Learning algorithms
/ Machine Learning
/ Meta-analysis
/ Psychiatry
/ Risk factors
/ Search engines
/ Self destructive behavior
/ Statistical methods
/ Suicide
/ Suicides & suicide attempts
2016
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Do you wish to request the book?
Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)
by
Ribeiro, J. D.
, Fox, K. R.
, Kleiman, E. M.
, Chang, B. P.
, Bentley, K. H.
, Franklin, J. C.
, Nock, M. K.
in
Algorithms
/ Anxiety
/ Artificial intelligence
/ Classification
/ Clinical psychology
/ Emotional disorders
/ Humans
/ Learning
/ Learning algorithms
/ Machine Learning
/ Meta-analysis
/ Psychiatry
/ Risk factors
/ Search engines
/ Self destructive behavior
/ Statistical methods
/ Suicide
/ Suicides & suicide attempts
2016
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Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)
Journal Article
Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016)
2016
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Overview
[...]treatment usage has increased among individuals who engage in SITBs in recent decades; despite this, rates of suicidal thoughts and behaviors have remained virtually unchanged (Kessler et al. 2005). [...]existing evidence indicates that prior psychiatric treatment is associated with increased (rather than decreased) rates of future suicidal thoughts and behaviors (e.g. Dahlsgaard et al. 1998; Qin & Nordentoft, 2005). [...]we propose that studies focused on suicidal thought and behavior prediction should prioritize the development of risk algorithms over risk factors.
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