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10.1017/S0033291715001774 http://scihub22266oqcxt.onion/10.1017/S0033291715001774 C5111361!5111361 !26436603     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26436603 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Psychol+Med 2016 ; 46 (2 ): 303-16 Nephropedia Template TP {{tp|p=26436603 |t=2016. Predicting non-familial major physical violent crime perpetration in the US Army from administrative data |pdf=|usr=}}{{26436603 }} gab.com Text Predicting non-familial major physical violent crime perpetration in the US Army from administrative data JO: Psychol Med http://www.kidney.de/pget.php?&tw=1&pmid=26436603 #FOAvip BACKGROUND: Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. METHOD: A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS: Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS: Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. Twit Text FOAVip Predicting non-familial major physical violent crime perpetration in the US Army from administrative data ????Psychol Med http://www.kidney.de/pget.php?&tw=1&pmid=26436603 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26436603 #FOAvip English Wikipedia <ref>{{Cite pmid|26436603 }}</ref> Predicting non-familial major physical violent crime perpetration in the US Army from administrative data #MMPMID26436603 Rosellini AJ ; Monahan J ; Street AE ; Heeringa SG ; Hill ED ; Petukhova M ; Reis BY ; Sampson NA ; Bliese P ; Schoenbaum M ; Stein MB ; Ursano RJ ; Kessler RC Psychol Med 2016[Jan]; 46 (2 ): 303-16 PMID26436603 show ga BACKGROUND: Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. METHOD: A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS: Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS: Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks. |*Social Class [MESH] |Adolescent [MESH] |Adult [MESH] |Age Factors [MESH] |Area Under Curve [MESH] |Crime/statistics & numerical data [MESH] |Female [MESH] |Firesetting Behavior/*epidemiology [MESH] |Homicide/*statistics & numerical data [MESH] |Humans [MESH] |Machine Learning [MESH] |Male [MESH] |Mental Disorders/*epidemiology/therapy [MESH] |Middle Aged [MESH] |Military Personnel/*statistics & numerical data [MESH] |Odds Ratio [MESH] |ROC Curve [MESH] |Regression Analysis [MESH] |Risk Assessment [MESH] |United States/epidemiology [MESH] |Violence/*statistics & numerical data [MESH]Predicting non-familial major physical violent crime perpetration in t(epmc).pdf DeepDyve Pubget Overpricing