ArticlePloS one2020
Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes.Journal of machine learning research : JMLR · 2023Article
- Making Sense of the Matrix: A Qualitative Assessment and Commentary on Connecting Psychiatric Symptom Scale Items to the Research Domain Criteria (RDoC).Innovations in clinical neuroscienceArticle
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Authors and funding
9 authors.
Funding
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 characteristic curve 0.91, 95% CI (0.88,0.94) using logistic regression. Derived symptom measures and estimated Research Domain Criteria scores also associated significantly with performance on neurocognitive tests.
conclusionsOur results support the potential utility of deriving dimensional phenotypic measures in psychiatric illness through the use of word embeddings, while illustrating the challenges in identifying truly orthogonal dimensions.
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