Profile Generators: A Link between the Narrative and the Binary Matrix Representation

TL;DR

This paper introduces a novel symptom profile generator that links narrative DSM-5 descriptions with binary matrix representations, enabling efficient and automated generation of symptom combinations for complex cognitive disorders.

Contribution

It develops a new generator format that simplifies the representation and analysis of complex mental health disorder symptom profiles, overcoming limitations of traditional binary matrices.

Findings

The generator format is readable, adaptable, and comprehensive.

It enables automated generation of symptom profiles for complex disorders.

The profile reduction method allows targeted similarity calculations.

Abstract

Mental health disorders, particularly cognitive disorders defined by deficits in cognitive abilities, are described in detail in the DSM-5, which includes definitions and examples of signs and symptoms. A simplified, machine-actionable representation was developed to assess the similarity and separability of these disorders, but it is not suited for the most complex cases. Generating or applying a full binary matrix for similarity calculations is infeasible due to the vast number of symptom combinations. This research develops an alternative representation that links the narrative form of the DSM-5 with the binary matrix representation and enables automated generation of valid symptom combinations. Using a strict pre-defined format of lists, sets, and numbers with slight variations, complex diagnostic pathways involving numerous symptom combinations can be represented. This format, called the symptom profile generator (or simply generator), provides a readable, adaptable, and comprehensive alternative to a binary matrix while enabling easy generation of symptom combinations (profiles). Cognitive disorders, which typically involve multiple diagnostic criteria with several symptoms, can thus be expressed as lists of generators. Representing several psychotic disorders in generator form and generating all symptom combinations showed that matrix representations of complex disorders become too large to manage. The MPCS (maximum pairwise cosine similarity) algorithm cannot handle matrices of this size, prompting the development of a profile reduction method using targeted generator manipulation to find specific MPCS values between disorders. The generators allow easier creation of binary representations for large matrices and make it possible to calculate specific MPCS cases between complex disorders through conditional generators.