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Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

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Summary
This summary is machine-generated.

This study aims to develop objective speech biomarkers for psychiatric disorders by analyzing linguistic features in Japanese speech data. The goal is to improve diagnosis and severity assessment for conditions like schizophrenia and depression.

Keywords:
biomarkerlanguagemachine learningnatural language processing (computer science)neurocognitive disorderspsychiatric disorders

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Neuroscience

Background:

  • Current psychiatric diagnoses rely on subjective observations, leading to potential inconsistencies.
  • Objective biomarkers for psychiatric disorders are lacking in clinical practice.
  • Linguistic features in speech show potential as indicators for certain psychiatric conditions.

Purpose of the Study:

  • To create a large dataset of Japanese speech data for psychiatric and neurocognitive disorders.
  • To quantify linguistic features associated with these disorders using natural language processing.
  • To develop objective and user-friendly biomarkers for diagnosis and severity assessment.

Main Methods:

  • Multi-center prospective study design.
  • Inclusion criteria based on DSM-5/ICD-11 for major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder, and neurocognitive disorders.
  • Speech data collected via interviews (free conversation, picture description, storytelling) from >400 participants, with clinical rating scales for severity assessment.

Main Results:

  • Collected over 1000 speech records from >400 participants as of September 2022.
  • Data represents one of the largest speech datasets in this field.
  • Addressing challenges in inter-rater reliability and Japanese linguistic peculiarities.

Conclusions:

  • The study's large dataset and novel methodology offer potential for developing objective biomarkers.
  • Findings could significantly enhance the objectivity and ease of diagnosing and assessing psychiatric disorders.
  • Future applications may extend to various fields requiring objective patient assessment.