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.
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