ArticleIEEE journal of biomedical and health informatics2017
Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.
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 24 papers, 4 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Towards text mining therapeutic change: A systematic review of text-based methods for Therapeutic Change Process Research.PloS one · 2019Pooled it
- Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach.Psychological medicine · 2023Trial
- Scoping review on natural language processing applications in counselling and psychotherapy.British journal of psychology (London, England : 1953) · 2026Article
- Natural language processing models for predicting treatment outcomes in internet-delivered cognitive behavioral therapy.Internet interventions · 2025Article
- Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis.Journal of medical Internet research · 2025Article
- Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.Communications medicine · 2024Article
- Investigating Patient Satisfaction Through Web-Based Reviews of Norwegian Dentists: Quantitative Study Using the Meaning Extraction Method.Journal of participatory medicine · 2024Article
- Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model.Journal of anxiety disorders · 2024Article
- Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions.Journal of healthcare informatics research · 2023Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study.Journal of medical Internet research · 2022Article
- A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
- The promise of machine learning in predicting treatment outcomes in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2021Article
- Analysis of the Emails From the Dutch Web-Based Intervention "Alcohol de Baas": Assessment of Early Indications of Drop-Out in an Online Alcohol Abuse Intervention.Frontiers in psychiatry · 2021Article
- A Framework for Applying Natural Language Processing in Digital Health Interventions.Journal of medical Internet research · 2020Article
- Incorporating Information From Electronic and Social Media Into Psychiatric and Psychotherapeutic Patient Care: Survey Among Clinicians.Journal of medical Internet research · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.
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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.