首页|Data from Laureate Institute for Brain Research Provide New Insights into Machin e Learning (Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women)
Data from Laureate Institute for Brain Research Provide New Insights into Machin e Learning (Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Tulsa, Oklahoma, by NewsRx j ournalists, research stated, “IntroductionIn the US, women are one of the fastes t-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversio n programs can be effective.” The news correspondents obtained a quote from the research from Laureate Institu te for Brain Research: “It may be beneficial to identify individuals who are mos t likely to complete the program versus terminate early as this can provide info rmation regarding who may need additional or unique programming to improve the l ikelihood of successful program completion. Prior research investigating predict ion of success in these programs has primarily focused on demographic factors in male samples. MethodsThe current study used machine learning (ML) to examine ot her non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, sel f-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, us ing repeated, nested cross-validation, predicting subsequent graduation versus t ermination from the program. An identical ML analysis was conducted for model tw o, in which 34 variables were entered, including the Women’s Risk/Needs Assessme nt (WRNA).ResultsML models were unable to predict graduation at an individual le vel better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses i ndicated measures of impulsivity, trauma history, interoceptive awareness, emplo yment/financial risk, housing safety, antisocial friends, anger/hostility, and W RNA total score and risk scores exhibited medium to large effect sizes in predic ting treatment completion (p <0.05; ds = 0.29 to 0.81).”
Laureate Institute for Brain ResearchT ulsaOklahomaUnited StatesNorth and Central AmericaBusinessCyborgsEme rging TechnologiesMachine Learning
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