Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec

Abstract

BackgroundRecent 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. MethodsWe 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 syntactic meaning. A low-dimensional approximation to the embedding space was used to derive the proposed succinct summary of symptom profiles. To validate our embedding-based disease profiles, these were compared to presence or absence of axis I diagnoses derived from structured clinical interview, and to objective neurocognitive testing. ResultsUnsupervised and supervised classification to distinguish presence/absence of axis I disorders using survey-level embeddings remained discriminative, with area under the receiver operating characteristic curve up to 0.85, 95% confidence interval (CI) (0.74,0.91) using Gaussian mixture modeling, and cross-validated area under the receiver operating

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“…Word embeddings have been utilized in a wide variety of fields such as artificial intelligence [12], information retrieval [13,14], sentiment analyses [15], and psychiatric [16]. There are several word embedding techniques such as Word2Vec, Glove, Latent Semantic Analysis (LSA), Doc2Vec, skip-gram, fasttex.…”