ArticleNPJ digital medicine2026
Machine-actionable criteria chart the symptom space of mental disorders.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Diagnostic rules are codified in consensus manuals such as DSM-5, yet they remain written in narrative form and cannot be computationally interrogated. Here, a deterministic framework is presented that translates diagnostic criteria into a machine-actionable representation of the full symptom space, which can be charted, navigated, and systematically analyzed. Unlike probabilistic models that infer patterns from large textual corpora, this framework directly interrogates explicit consensus criteria, providing a transparent and reproducible means of assessing conceptual coherence. Its potential is demonstrated by charting schizophrenia-spectrum disorders, which remain conceptually distinct despite substantial symptom overlap, and by evaluating the current National Academies' definition of Long COVID, which is largely subsumed by depressive and anxiety disorders. By making diagnostic consensus computable, the framework provides a reproducible foundation for evaluating delineation properties of existing and candidate diagnostic constructs and for developing interpretable, regulatory-compliant diagnostic support tools.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.