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Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. / Bobo, Samson; Kolonin, Anton.

Advances in Neural Computation, Machine Learning, and Cognitive Research IX. ed. / Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladimir Redko; Yury Tiumentsev; Valentin V. Klimov. Springer, 2026. p. 545-562 43 (Studies in Computational Intelligence; Vol. 1241 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Bobo, S & Kolonin, A 2026, Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. in B Kryzhanovsky, W Dunin-Barkowski, V Redko, Y Tiumentsev & VV Klimov (eds), Advances in Neural Computation, Machine Learning, and Cognitive Research IX., 43, Studies in Computational Intelligence, vol. 1241 SCI, Springer, pp. 545-562, XXVII International Conference on Neuroinformatics, Москва, Russian Federation, 20.10.2025. https://doi.org/10.1007/978-3-032-07690-8_43

APA

Bobo, S., & Kolonin, A. (2026). Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. In B. Kryzhanovsky, W. Dunin-Barkowski, V. Redko, Y. Tiumentsev, & V. V. Klimov (Eds.), Advances in Neural Computation, Machine Learning, and Cognitive Research IX (pp. 545-562). [43] (Studies in Computational Intelligence; Vol. 1241 SCI). Springer. https://doi.org/10.1007/978-3-032-07690-8_43

Vancouver

Bobo S, Kolonin A. Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. In Kryzhanovsky B, Dunin-Barkowski W, Redko V, Tiumentsev Y, Klimov VV, editors, Advances in Neural Computation, Machine Learning, and Cognitive Research IX. Springer. 2026. p. 545-562. 43. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-07690-8_43

Author

Bobo, Samson ; Kolonin, Anton. / Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. Advances in Neural Computation, Machine Learning, and Cognitive Research IX. editor / Boris Kryzhanovsky ; Witali Dunin-Barkowski ; Vladimir Redko ; Yury Tiumentsev ; Valentin V. Klimov. Springer, 2026. pp. 545-562 (Studies in Computational Intelligence).

BibTeX

@inproceedings{2a22cd3f15044dad8f50ebe7ea77a955,
title = "Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives",
abstract = "This paper introduces an unsupervised machine learning framework for detecting CDs in psychotherapy transcripts. Our novel pipeline integrates semantic embedding using MiniLM-L6-v2, Principal Component Analysis (75 orthogonal directions, PCA75), optimized HDBSCAN clustering (silhouette score = 0.098), and KeyBERT-assisted clinical interpretation. Analysis of 6,057 patient narratives reveals three dominant CD profiles: Social Anxiety with (64.9% distorted utterances), Performance Anxiety (100% distorted utterances), and Mixed Symptoms (noise cluster, r = −0.30). Clinical validation by three licensed psychologists evaluating 100 samples per cluster demonstrates strong cluster coherence (Fleiss{\textquoteright} κ = 0.68, indicating “substantial agreement” per Landis and Koch, 1977). The framework provides clinicians with a scalable taxonomy-free tool for cognitive pattern identification, enabling more efficient treatment personalization and progress monitoring.",
keywords = "Clinical intepretation, Cognitive Distortions, Unsupervised learning",
author = "Samson Bobo and Anton Kolonin",
note = "Bobo, S., Kolonin, A. (2026). Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IX. NEUROINFORMATICS 2025. Studies in Computational Intelligence, vol 1241. Springer, Cham. https://doi.org/10.1007/978-3-032-07690-8_43; XXVII International Conference on Neuroinformatics ; Conference date: 20-10-2025 Through 24-10-2025",
year = "2026",
doi = "10.1007/978-3-032-07690-8_43",
language = "English",
isbn = "978-3-032-07689-2",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "545--562",
editor = "Boris Kryzhanovsky and Witali Dunin-Barkowski and Vladimir Redko and Yury Tiumentsev and Klimov, {Valentin V.}",
booktitle = "Advances in Neural Computation, Machine Learning, and Cognitive Research IX",
address = "United States",

}

RIS

TY - GEN

T1 - Unsupervised Learning for Detection of Cognitive Distortions in Patient Narratives

AU - Bobo, Samson

AU - Kolonin, Anton

N1 - Conference code: 27

PY - 2026

Y1 - 2026

N2 - This paper introduces an unsupervised machine learning framework for detecting CDs in psychotherapy transcripts. Our novel pipeline integrates semantic embedding using MiniLM-L6-v2, Principal Component Analysis (75 orthogonal directions, PCA75), optimized HDBSCAN clustering (silhouette score = 0.098), and KeyBERT-assisted clinical interpretation. Analysis of 6,057 patient narratives reveals three dominant CD profiles: Social Anxiety with (64.9% distorted utterances), Performance Anxiety (100% distorted utterances), and Mixed Symptoms (noise cluster, r = −0.30). Clinical validation by three licensed psychologists evaluating 100 samples per cluster demonstrates strong cluster coherence (Fleiss’ κ = 0.68, indicating “substantial agreement” per Landis and Koch, 1977). The framework provides clinicians with a scalable taxonomy-free tool for cognitive pattern identification, enabling more efficient treatment personalization and progress monitoring.

AB - This paper introduces an unsupervised machine learning framework for detecting CDs in psychotherapy transcripts. Our novel pipeline integrates semantic embedding using MiniLM-L6-v2, Principal Component Analysis (75 orthogonal directions, PCA75), optimized HDBSCAN clustering (silhouette score = 0.098), and KeyBERT-assisted clinical interpretation. Analysis of 6,057 patient narratives reveals three dominant CD profiles: Social Anxiety with (64.9% distorted utterances), Performance Anxiety (100% distorted utterances), and Mixed Symptoms (noise cluster, r = −0.30). Clinical validation by three licensed psychologists evaluating 100 samples per cluster demonstrates strong cluster coherence (Fleiss’ κ = 0.68, indicating “substantial agreement” per Landis and Koch, 1977). The framework provides clinicians with a scalable taxonomy-free tool for cognitive pattern identification, enabling more efficient treatment personalization and progress monitoring.

KW - Clinical intepretation

KW - Cognitive Distortions

KW - Unsupervised learning

UR - https://www.scopus.com/pages/publications/105020041365

UR - https://www.mendeley.com/catalogue/3cb244c5-f926-396b-87be-17b38a673c57/

U2 - 10.1007/978-3-032-07690-8_43

DO - 10.1007/978-3-032-07690-8_43

M3 - Conference contribution

SN - 978-3-032-07689-2

T3 - Studies in Computational Intelligence

SP - 545

EP - 562

BT - Advances in Neural Computation, Machine Learning, and Cognitive Research IX

A2 - Kryzhanovsky, Boris

A2 - Dunin-Barkowski, Witali

A2 - Redko, Vladimir

A2 - Tiumentsev, Yury

A2 - Klimov, Valentin V.

PB - Springer

T2 - XXVII International Conference on Neuroinformatics

Y2 - 20 October 2025 through 24 October 2025

ER -

ID: 71986474