Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.

ABSTRACT: BACKGROUND:Recent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. METHODS:We propose a method for applying natural language processing to derive dimensional measures of psychiatric symptoms from questionnaire data. We utilized nine self-report symptom measures drawn from a large cellular biobanking study that enrolled individuals with mood and psychotic disorders, as well as healthy controls. To summarize questionnaire results we used word embeddings, a technique to represent words as numeric vectors preserving semantic and

SUBMITTER: Sonabend W A 

PROVIDER: S-EPMC7122719 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature