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This study introduces a novel natural language processing method using word embeddings to create dimensional measures of psychiatric symptoms from questionnaires. This approach effectively distinguishes psychiatric disorders and correlates with neurocognitive performance.

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Area of Science:

  • Computational psychiatry
  • Natural language processing in mental health
  • Dimensional phenotyping

Background:

  • Psychiatric research increasingly focuses on multidimensional symptom characterization.
  • Standard self-report scales for multiaxial psychiatric assessment require further study, especially for supporting categorical and dimensional phenotypes.

Purpose of the Study:

  • To propose and validate a natural language processing (NLP) method for deriving dimensional measures of psychiatric symptoms from questionnaire data.
  • To assess the utility of word embeddings for summarizing symptom profiles and supporting psychiatric phenotyping.

Main Methods:

  • Utilized nine self-report symptom measures from a large biobanking study involving individuals with mood/psychotic disorders and healthy controls.
  • Applied word embeddings to represent questionnaire data numerically, preserving semantic meaning.
  • Derived succinct symptom profiles using low-dimensional approximations of the embedding space.
  • Validated embedding-based profiles against Axis I diagnoses and neurocognitive testing.

Main Results:

  • Unsupervised and supervised classification models using survey-level embeddings achieved high discriminative accuracy for Axis I disorders (AUC up to 0.91).
  • Derived symptom measures and estimated Research Domain Criteria (RDoC) scores showed significant associations with neurocognitive test performance.

Conclusions:

  • Word embeddings show potential for deriving dimensional phenotypic measures in psychiatric illness.
  • The study highlights challenges in identifying truly orthogonal dimensions for psychiatric assessment.