Machine-actionable criteria chart the symptom space of mental disorders

Diagnostic manuals encode community consensus in prose yet offer no direct means to computationally evaluate the conceptual integrity of disorder definitions. We introduce a machine-actionable framework that translates narrative diagnostic criteria into the full symptom space, the exhaustive set of symptom combinations valid for each disorder. This approach enables charting of how these symptom spaces intersect, diverge, or subsume one another. Applied to representative DSM-5 disorders and to the emerging definition of Long COVID, the framework confirms clear boundaries among established disorders while highlighting substantial conceptual redundancy between Long COVID and mood or anxiety disorders. Whereas probabilistic models infer patterns from broad textual corpora, the proposed framework directly interrogates explicit consensus criteria, providing a transparent and reproducible means of assessing conceptual coherence. By making consensus-based diagnostic knowledge computable, the framework supports the refinement of classification systems and provides a foundation for interpretable clinical decision support.