Using Complete Enumeration to Derive “One-Size-Fits-All” Versus “Subgroup-Specific” Diagnostic Rules for Substance Use Disorder

Abstract

The use of fixed diagnostic rules, whereby the same diagnostic algorithms are applied across all individuals regardless of personal attributes, has been the tradition in the Diagnostic and Statistical Manual of Mental Disorders. This practice of “averaging” across individuals inevitably introduces diagnostic error. Furthermore, these average rules are typically derived through expert consensus rather than through data-driven approaches. Utilizing National Survey on Drug Use and Health 2013 ( N = 23, 889), we examined whether subgroup-specific, “customized” alcohol use disorder diagnostic rules, derived using deterministic optimization, perform better than an average, “one-size-fits-all” diagnostic rule. The average solution for the full sample included a set size of six and diagnostic threshold of three. Subgroups had widely varying set sizes ( M = 6.870; range = 5-10) with less varying thresholds ( M = 2.70; range = 2-4). External validation verified that the customized algorithms performed as well, and sometimes better than, the average solution in the prediction of relevant correlates. However, the average solution still performed adequately with respect to external validators.

ParTy ’n’ Play: Associations Between Sex-Related Substance Use and the Disinhibited Externalizing Spectrum Among Gay and Bisexual Men

“…Much of the measurement and many of the criteria for diagnosis of problematic substance use have been normed on general population (i.e., heterosexual rather than sexual minority) samples. Additional measurement studies are needed to identify appropriate reference values within sexual minority men as there is evidence to suggest that subgroup-specific diagnostic rules for substance use disorder may be more optimal than "one-size-fits-all" approaches (Boness et al, 2020). Perhaps, for sexual minority men too, it is worth reviewing existing criteria for disorders and empirically evaluating the clinical utility of these measures for this specific population.…”

Um Modelo para Predição Individualizada da Deterioração da Saúde de Pacientes no âmbito da Internet das Coisas

“…O desenvolvimento de estratégias personalizadas de cuidado com a saúde em diferentes populações e ambientesé um dos componentes da medicina de precisão [Martha et al 2020]. No entanto, quando o AMé utilizado para treinar modelos de predição em saúde, uma estratégia comumente aplicadaé a populacional ou one-size-fitsall onde o diagnóstico e o tratamento são os mesmos para todos os pacientes sem distinção entre aspectos individuais do paciente como idade, gênero e etnia [Boness et al 2020].…”