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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Schizophrenia is a complex mental health disorder that can manifest with various positive symptoms, including thought, movement, and behavior disorders. These symptoms significantly disrupt cognitive and motor functions, leading to profound effects on an individual's ability to engage with the world.
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Quantified language connectedness in schizophrenia-spectrum disorders.

A E Voppel1, J N de Boer2, S G Brederoo1

  • 1Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Psychiatry Research
|July 31, 2021
PubMed
Summary
This summary is machine-generated.

Natural language processing can identify schizophrenia-spectrum disorders by analyzing speech connectedness. This method achieved 85% accuracy, showing potential as a digital diagnostic tool.

Keywords:
BiomarkerNatural language processingPsychosisSemantic modelSpeechWord similarity

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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Neuroscience

Background:

  • Language abnormalities are a key symptom of schizophrenia-spectrum disorders.
  • Quantifying language connectedness using natural language processing (NLP) may reveal diagnostic markers.
  • Previous research suggests reduced language connectedness in schizophrenia.

Purpose of the Study:

  • To investigate spontaneous speech connectedness in schizophrenia-spectrum patients versus controls.
  • To determine the accuracy of language connectedness in classifying schizophrenia-spectrum disorder.
  • To identify specific features of language connectedness that discriminate between patients and controls.

Main Methods:

  • Recorded spontaneous speech from 50 schizophrenia-spectrum patients and 50 controls via semi-structured interviews.
  • Calculated language connectedness using a semantic word2vec model with consecutive word similarity in moving windows (2-20 words).
  • Utilized mean, minimal similarity, and variance per window size as features for a random forest classifier.

Main Results:

  • The classifier achieved 85% cross-validated accuracy, with 84% specificity and 86% sensitivity in distinguishing patients from controls.
  • Variance of similarity within window sizes of 5-10 words were the most discriminative features.
  • Impaired language connectedness was observed in schizophrenia-spectrum patients, even those with low positive symptom ratings, particularly at the sentence level.

Conclusions:

  • Spontaneous speech connectedness analysis is an accurate and feasible method for aiding in the diagnosis of schizophrenia-spectrum disorders.
  • This NLP-based approach demonstrates high sensitivity and specificity, offering a potential digital diagnostic assistant.
  • The findings highlight the utility of analyzing subtle language features for psychiatric diagnosis.