Using Complete Enumeration to Derive "One-Size-Fits-All" Versus "Subgroup-Specific" Diagnostic Rules for Substance...

Area of Science:

• Psychiatry
• Psychology
• Data Science

Background:

• Traditional diagnostic manuals like the Diagnostic and Statistical Manual of Mental Disorders (DSM) use fixed rules applied universally.
• This
• averaging
• approach can lead to diagnostic errors by not accounting for individual differences.
• Current diagnostic algorithms are often based on expert consensus rather than empirical data.

Purpose of the Study:

• To investigate whether subgroup-specific, data-driven diagnostic rules for alcohol use disorder (AUD) outperform average, one-size-fits-all rules.
• To assess the diagnostic accuracy and predictive validity of customized versus standardized diagnostic algorithms.

Main Methods:

• Utilized data from the National Survey on Drug Use and Health 2013 (N = 23,889).
• Employed deterministic optimization to derive subgroup-specific diagnostic rules for AUD.
• Compared the performance of customized algorithms against an average, population-level diagnostic rule using external validation.

Main Results:

• Subgroup-specific rules exhibited varying set sizes (range: 5-10) and thresholds (range: 2-4), differing from the average solution (set size: 6, threshold: 3).
• Customized algorithms demonstrated comparable or superior performance to the average solution in predicting relevant correlates.
• The average, one-size-fits-all rule maintained adequate performance on external validation metrics.

Conclusions:

• Subgroup-specific diagnostic algorithms derived through data-driven methods offer improved accuracy for alcohol use disorder (AUD) diagnosis.
• Tailoring diagnostic rules to specific populations can enhance clinical utility and reduce diagnostic error.
• While customized rules show advantages, the traditional average approach remains a viable, albeit less precise, diagnostic method.