2023
DOI: 10.1007/s11121-023-01561-x
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Comparing Factors Associated with Increased Stimulant Use in Relation to HIV Status Using a Machine Learning and Prediction Modeling Approach

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“…The effects of social adversity and the efficacy of interventions in NHB PLWH and MSM living with HIV support Dr. Wakim-Takaki's biopsychosocial model, suggesting the need to address this dual diagnosis in a population-centered manner by addressing social, behavioral, and neurological mechanisms [15]. Machine learning is becoming a useful tool to identify biological, behavioral, and social determinants of SUD and HIV independently; however, to our knowledge, it has not been applied to identify predictors of comorbid HIV and SUD [193][194][195][196]. Data-driven models that predict determinants of comorbid HIV and SUDs will inform researchers and clinicians on developing more comprehensive diagnostic testing and population-and patient-centered care.…”
Section: Discussionmentioning
confidence: 99%
“…The effects of social adversity and the efficacy of interventions in NHB PLWH and MSM living with HIV support Dr. Wakim-Takaki's biopsychosocial model, suggesting the need to address this dual diagnosis in a population-centered manner by addressing social, behavioral, and neurological mechanisms [15]. Machine learning is becoming a useful tool to identify biological, behavioral, and social determinants of SUD and HIV independently; however, to our knowledge, it has not been applied to identify predictors of comorbid HIV and SUD [193][194][195][196]. Data-driven models that predict determinants of comorbid HIV and SUDs will inform researchers and clinicians on developing more comprehensive diagnostic testing and population-and patient-centered care.…”
Section: Discussionmentioning
confidence: 99%