Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging...

Area of Science:

• Psychiatry
• Computational Linguistics
• Artificial Intelligence

Background:

• Schizophrenia relapse negatively impacts long-term health and functional recovery.
• Current relapse prediction methods lack the necessary specificity and sensitivity for timely intervention.
• High relapse rates persist despite existing early intervention programs, necessitating novel approaches.

Purpose of the Study:

• To review the application of Natural Language Processing (NLP) in speech analysis for predicting recurrent psychotic episodes in schizophrenia.
• To highlight NLP's potential as an accurate and timely tool for schizophrenia relapse prediction.

Main Methods:

• Review of recent advancements in Natural Language Processing (NLP) applied to speech analysis.
• Identification of linguistic markers indicative of thought disorder and language disruptions preceding relapse.
• Exploration of remote monitoring technologies for psychotic relapse detection.

Main Results:

• Natural Language Processing (NLP) of speech can detect specific linguistic markers associated with thought disorder.
• These markers appear 2-4 weeks prior to a psychotic relapse, indicating a significant lead time.
• Individual speech patterns can be captured, demonstrating the potential for personalized prediction.

Conclusions:

• Speech analysis using Natural Language Processing (NLP) shows promise as an accurate tool for predicting schizophrenia relapse.
• This approach offers sufficient construct validity and lead time for proactive clinical interventions.
• NLP-based speech analysis presents a viable alternative for remote monitoring and timely relapse prevention in schizophrenia.