ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
The Centroid Cannot Hold: Comparing Sequential and Global Estimates of Coherence as Indicators of Formal Thought Disorder.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.Neuropsychologia · 2026Article
- Reduced linguistic coherence in psychosis defies semantic similarity accounts and relates to altered large-scale cortical hierarchy.Scientific reports · 2026Article
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- Perplexity and proximity: Large language model perplexity complements semantic distance metrics for the detection of incoherent speech.Journal of biomedical informatics · 2025Article
- Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder.... Workshop on Computational Linguistics and Clinical Psychology · 2025Article
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- Approximating the semantic space: word embedding techniques in psychiatric speech analysis.Schizophrenia (Heidelberg, Germany) · 2024Article
- Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS).Journal of biomedical informatics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Thought disorder (TD) as reflected in incoherent speech is a cardinal symptom of schizophrenia and related disorders. Quantification of the degree ofTD can inform diagnosis, monitoring, and timely intervention. Consequently, there has been an interest in applying methods ofdistributional semantics to quantify incoherence ofspoken language. Prior studies have generally involved few participants and utilized speech data collected in on-site structured interviews. In this paper we conduct a comprehensive evaluation ofapproaches to quantify incoherence using distributional semantics, including a novel variant that measures the global coherence oftext. This evaluation is conducted in the context of "audio diaries" collected from participants experiencing auditory verbal hallucinations using a smartphone application. Results reveal our novel global coherence metric using the centroid (weighted vector average) outperforms established approaches in their agreement with human annotators, supporting their preferential use in the context of short recordings ofunstructured and largely spontaneous speech.
Indexed as
Identifiers
33936508PMC8075468W3157868277What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.