Client and Therapist Psychotherapy Sentiment Interaction Throughout Therapy

Syzdek, Brian M.

doi:10.1007/s12646-020-00567-7

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Mutual affect analysis. Five interaction studies examined provider-therapist emotional convergence [ 131 , 132 ] during the intervention, including defense mechanisms [ 133 ] and humor [ 45 ]. Tanana and colleagues examined a large corpus of therapy transcripts [ 102 ], and showed that an attention-based architecture captured therapists’ and patients’ valence interactions and their context more accurately ( K = 0.48) than previous lexicon methods ( K = 0.25 and K = 0.31) and human raters ( K = 0.42).…”

Section: Resultsmentioning

confidence: 99%

Natural language processing for mental health interventions: a systematic review and research framework

Malgaroli,

Hull,

Zech

et al. 2023

Transl Psychiatry

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Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP’s potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients’ clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers’ characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

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Section: Resultsmentioning

confidence: 99%

Natural language processing for mental health interventions: a systematic review and research framework

Malgaroli,

Hull,

Zech

et al. 2023

Transl Psychiatry

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“…For example, topic models have been used to automatically assess motivational-interviewing fidelity (Atkins et al, 2014) and classify types of psychotherapy treatment used by providers (Imel, Steyvers, & Atkins, 2015). In addition, NLP models have been used to predict the sentiment of therapist and client statements (Syzdek, 2020;Tanana et al, 2016Tanana et al, , 2021. Furthermore, NLP has shown promise in predicting therapeutic processes such as therapist empathy (Xiao et al, 2012(Xiao et al, , 2015 and therapeutic alliance (Goldberg et al, 2020).…”

Section: Empirical Articlementioning

confidence: 99%

Machine-Learning-Based Prediction of Client Distress From Session Recordings

Kuo

Tanana

Goldberg

et al. 2023

Clinical Psychological Science

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Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist–client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client-outcome prediction that have (a) used transcripts of therapist–client interactions as direct predictors of client-symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman’s ρ = .32, p < .001). Our results highlight the potential for NLP models to be implemented in outcome-monitoring systems to improve quality of care. We discuss implications for future research and applications.

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“…Psychologists can then empower the patient to take an active role in their own recovery and enhance their motivation and satisfaction with the therapy. For instance, psychologists often use patient feedback to measure the effectiveness of the treatment approach [3][4][5][6]. However, such a manual analysis conducted by psychologists is sometimes inefficient and inconsistent across different psychologists.…”

Section: Introductionmentioning

confidence: 99%

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach (Preprint)

Wang,

Yu,

Liu

et al. 2023

Preprint

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BACKGROUND After the COVID-19 pandemic, the conflict between limited mental health care resources and the rapidly growing number of patients has become more pronounced. It is necessary for psychologists to borrow artificial intelligence (AI)–based methods to analyze patients’ satisfaction with drug treatment for those undergoing mental illness treatment. OBJECTIVE Our goal was to construct highly accurate and transferable models for predicting the satisfaction of patients with mental illness with medication by analyzing their own experiences and comments related to medication intake. METHODS We extracted 41,851 reviews in 20 categories of disorders related to mental illnesses from a large public data set of 161,297 reviews in 16,950 illness categories. To discover a more optimal structure of the natural language processing models, we proposed the Unified Interchangeable Model Fusion to decompose the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), support vector machine, and random forest (RF) models into 2 modules, the encoder and the classifier, and then reconstruct fused “encoder+classifer” models to accurately evaluate patients’ satisfaction. The fused models were divided into 2 categories in terms of model structures, traditional machine learning–based models and neural network–based models. A new loss function was proposed for those neural network–based models to overcome overfitting and data imbalance. Finally, we fine-tuned the fused models and evaluated their performance comprehensively in terms of F1-score, accuracy, κ coefficient, and training time using 10-fold cross-validation. RESULTS Through extensive experiments, the transformer bidirectional encoder+RF model outperformed the state-of-the-art BERT, MentalBERT, and other fused models. It became the optimal model for predicting the patients’ satisfaction with drug treatment. It achieved an average graded F1-score of 0.872, an accuracy of 0.873, and a κ coefficient of 0.806. This model is suitable for high-standard users with sufficient computing resources. Alternatively, it turned out that the word-embedding encoder+RF model showed relatively good performance with an average graded F1-score of 0.801, an accuracy of 0.812, and a κ coefficient of 0.695 but with much less training time. It can be deployed in environments with limited computing resources. CONCLUSIONS We analyzed the performance of support vector machine, RF, BERT, MentalBERT, and all fused models and identified the optimal models for different clinical scenarios. The findings can serve as evidence to support that the natural language processing methods can effectively assist psychologists in evaluating the satisfaction of patients with drug treatment programs and provide precise and standardized solutions. The Unified Interchangeable Model Fusion provides a different perspective on building AI models in mental health and has the potential to fuse the strengths of different components of the models into a single model, which may contribute to the development of AI in mental health.

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Client and Therapist Psychotherapy Sentiment Interaction Throughout Therapy

Cited by 7 publications

References 12 publications

Natural language processing for mental health interventions: a systematic review and research framework

Natural language processing for mental health interventions: a systematic review and research framework

Machine-Learning-Based Prediction of Client Distress From Session Recordings

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach (Preprint)

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