Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS)

Xu, Weizhe; Wang, Weichen; Portanova, Jake; Chander, Ayesha; Campbell, Andrew T.; Pakhomov, Serguei; Ben‐Zeev, Dror; Cohen, Trevor

doi:10.1016/j.jbi.2022.103998

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…Despite the fair diagnostic consistency and stability of psychiatric interviews (32,33), most mental illness diagnoses, including schizophrenia diagnosis, lack reliable biomarkers or validated methods to serve as objective auxiliary diagnostic tools (34)(35)(36)). We will build on this pilot study and implement a portable voice-assisted diagnostic tool, like Xu et al, who used smartphone audio recordings to detect incoherent speech (37).…”

Section: Discussionmentioning

confidence: 99%

Deep learning for schizophrenia classification based on natural language processing—A pilot study

Lin,

Chen,

Chang

et al. 2024

Preprint

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Background：The correct diagnosis of schizophrenia is essential to reduce the economic burden and avoid worsening patients’ comorbidities. However, current clinical diagnosis is subjective and time consuming. We propose a deep learning method using the bidirectional encoder representations from transformers (BERT) to identify lexical incoherence related to schizophrenia. Methods：We use a fine-tuned BERT model to extract schizophrenia-related text features and detect possible schizophrenia. Our study involves the enrollment of 13 participants diagnosed with schizophrenia and 13 participants without schizophrenia. Following the collection of speech data, we create a training set by sampling from 10 speakers in each group. Subsequently, the remaining speakers' data is reserved for external testing to assess the model's performance. Results：After adjusting the parameters of the BERT model, we achieve excellent detection results, with an average accuracy of 84%, 95% of true positives, and an F1 score of 0.806. These results underscore the efficacy of our proposed system in identifying lexical incoherence related to schizophrenia. Conclusions：Our proposed method, leveraging the deep learning BERT model, shows promise in contributing to schizophrenia diagnosis. The model's self-attention mechanism successfully extracts representative schizophrenia-related text features, providing an objective indicator for psychiatrists. With ongoing refinement, the BERT model serves as a valuable auxiliary tool for expedited and objective schizophrenia diagnosis, ultimately alleviating societal economic burdens and preventing major complications in patients.

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Section: Discussionmentioning

confidence: 99%

Deep learning for schizophrenia classification based on natural language processing—A pilot study

Lin,

Chen,

Chang

et al. 2024

Preprint

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“…Testing the usability of these measures in online settings with short speech samples would complement the work presented here. Furthermore, whilst our study focused on text-based language markers, numerous findings support the use of vocal, acoustic markers to assess psychotic symptoms from speech [10,[42][43][44], which were not included here. Other findings suggest the potential of other, remotely assessable markers coming from visual cues or passive sensing data of smart devices for monitoring or assessment [10,[42][43][44][45][46][47].…”

Section: Limitations and Further Researchmentioning

confidence: 99%

“…Furthermore, whilst our study focused on text-based language markers, numerous findings support the use of vocal, acoustic markers to assess psychotic symptoms from speech [10,[42][43][44], which were not included here. Other findings suggest the potential of other, remotely assessable markers coming from visual cues or passive sensing data of smart devices for monitoring or assessment [10,[42][43][44][45][46][47]. Testing these markers in online settings, especially in additive nature to text-based markers would be another important step to potentially improve the power and precision of automated, online assessment of the psychotic spectrum.…”

Section: Limitations and Further Researchmentioning

confidence: 99%

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures

Olah,

Cummins,

Arribas

et al. 2024

Transl Psychiatry

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Automatically extracted measures of speech constitute a promising marker of psychosis as disorganized speech is associated with psychotic symptoms and predictive of psychosis-onset. The potential of speech markers is, however, hampered by (i) lengthy assessments in laboratory settings and (ii) manual transcriptions. We investigated whether a short, scalable data collection (online) and processing (automated transcription) procedure would provide data of sufficient quality to extract previously validated speech measures. To evaluate the fit of our approach for purpose, we assessed speech in relation to psychotic-like experiences in the general population. Participants completed an 8-minute-long speech task online. Sample 1 included measures of psychometric schizotypy and delusional ideation (N = 446). Sample 2 included a low and high psychometric schizotypy group (N = 144). Recordings were transcribed both automatically and manually, and connectivity, semantic, and syntactic speech measures were extracted for both types of transcripts. 73%/86% participants in sample 1/2 completed the experiment. Nineteen out of 25 speech measures were strongly (r > 0.7) and significantly correlated between automated and manual transcripts in both samples. Amongst the 14 connectivity measures, 11 showed a significant relationship with delusional ideation. For the semantic and syntactic measures, On Topic score and the Frequency of personal pronouns were negatively correlated with both schizotypy and delusional ideation. Combined with demographic information, the speech markers could explain 11–14% of the variation of delusional ideation and schizotypy in Sample 1 and could discriminate between high-low schizotypy with high accuracy (0.72−0.70, AUC = 0.78–0.79) in Sample 2. The moderate to high retention rate, strong correlation of speech measures across manual and automated transcripts and sensitivity to psychotic-like experiences provides initial evidence that online collected speech in combination with automatic transcription is a feasible approach to increase accessibility and scalability of speech-based assessment of psychosis.

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“…In relation to this, it would be necessary to analyze what procedure to calculate the connectives-related similarity is the most reliable, valid and theoretically sounded. More recent computational semantic representations could be used to obtain the word embeddings for analyses, such as Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al 2019), possibly in combination with time-series analyses of semantic coherence (Xu et al 2022). Also, mixed designs (e.g., Holm et al 2016;Saavedra 2010) might provide valuable details overlooked by quantitative approaches alone, increasing our comprehension of how thematic continuity and syntactic connectivity (in)dependently build up (in)coherence in patients with SSD.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

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Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS)

Cited by 12 publications

References 46 publications

Deep learning for schizophrenia classification based on natural language processing—A pilot study

Deep learning for schizophrenia classification based on natural language processing—A pilot study

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures

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