Therapists and psychotherapy side effects in China: A machine learning-based study

Yao, Lijun; Xu, Zhiwei; Zhao, Xudong; Chen, Yang; Liu, Liang; Fu, Xiaoming; Chen, Fazhan

doi:10.1016/j.heliyon.2022.e11821

Cited by 1 publication

(1 citation statement)

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“…For example, a recent study trained several machine learning algorithms on patients' self-reported side effects of psychotherapy to predict performances of the psychotherapeutic outcomes. They achieved an accuracy of 79.7% using Random Forest-based machine learning classifiers [58]. However, such implementation of prediction classifiers is to be used with caution as numerous biases might be implied such as class imbalances and the definition of the therapeutic outcome in their context of relevance.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia

Hudon,

Beaudoin,

Phraxayavong

et al. 2023

JPM

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(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients’ needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT.

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Section: Discussionmentioning

confidence: 99%