ArticleIEEE journal of biomedical and health informatics2017
Content Coding of Psychotherapy Transcripts Using Labeled Topic Models.
Article in IEEE journal of biomedical and health informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.Prevention science : the official journal of the Society for Prevention Research · 2026Trial
- Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT).Psychological medicine · 2025Trial
- Scoping review on natural language processing applications in counselling and psychotherapy.British journal of psychology (London, England : 1953) · 2026Article
- Psychotherapist remarks' ML classifier: insights from LLM and topic modeling application.Frontiers in psychiatry · 2025Article
- Machine Learning-Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls.Psychiatric services (Washington, D.C.) · 2024Article
- Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.Cognitive and behavioral practice · 2024Article
- Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?Addiction science & clinical practice · 2024Article
- Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.JAMA network open · 2024Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review.Journal of medical Internet research · 2023Article
- Towards automatic text-based estimation of depression through symptom prediction.Brain informatics · 2023Article
- Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research.Frontiers in psychiatry · 2023Review
- Coregulation of therapist and client emotion during psychotherapy.Psychotherapy research : journal of the Society for Psychotherapy Research · 2020Article
- Using artificial intelligence to analyse and teach communication in healthcare.Breast (Edinburgh, Scotland) · 2020Article
- Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.Psychotherapy (Chicago, Ill.) · 2019Article
- Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.Journal of healthcare informatics research · 2019Article
- A case-controlled field study evaluating ICD-11 proposals for relational problems and intimate partner violence.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, nonstandardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the labeled latent Dirichlet allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of 0.79, and 0.70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scalable method for accurate automated coding of psychotherapy sessions that perform better than comparable discriminative methods at session-level coding and can also predict fine-grained codes.
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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.