Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach.
Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or less likely to have a positive response to CBT. Here...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0313351 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850261253723783168 |
|---|---|
| author | Qasim Bukhari David Rosenfield Stefan G Hofmann John D E Gabrieli Satrajit S Ghosh |
| author_facet | Qasim Bukhari David Rosenfield Stefan G Hofmann John D E Gabrieli Satrajit S Ghosh |
| author_sort | Qasim Bukhari |
| collection | DOAJ |
| description | Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or less likely to have a positive response to CBT. Here, we used machine learning on data from 157 patients to examine whether individual patient responses to CBT can be predicted based on demographic information, psychiatric history, and self-reported or clinician-reported scales, subscales and questionnaires acquired prior to treatment. Machine learning models were able to explain about 26% of the variance in final treatment improvements. To assess generalizability, we evaluated multiple machine learning models using cross-validation and determined which input features were essential for prediction. While prediction accuracy was similar across models, the importance of specific features varied across models. In general, the combination of total scale score, subscale scores and responses to individual questions on a severity measure, the Liebowitz Social Anxiety Scale (LSAS), was the most informative in achieving the highest predictions that alone accounted for about 26% of the variance in treatment outcome. Demographic information, psychiatric history, personality measures, other self-reported or clinician-reported questionnaires, and clinical scales related to anxiety, depression, and quality of life provided no additional predictive power. These findings indicate that combining scaled and individual responses to LSAS questions are informative for predicting individual response to CBT in patients with SAD. |
| format | Article |
| id | doaj-art-e086f182eec2479583d85e678654eece |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e086f182eec2479583d85e678654eece2025-08-20T01:55:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031335110.1371/journal.pone.0313351Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach.Qasim BukhariDavid RosenfieldStefan G HofmannJohn D E GabrieliSatrajit S GhoshOnly about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or less likely to have a positive response to CBT. Here, we used machine learning on data from 157 patients to examine whether individual patient responses to CBT can be predicted based on demographic information, psychiatric history, and self-reported or clinician-reported scales, subscales and questionnaires acquired prior to treatment. Machine learning models were able to explain about 26% of the variance in final treatment improvements. To assess generalizability, we evaluated multiple machine learning models using cross-validation and determined which input features were essential for prediction. While prediction accuracy was similar across models, the importance of specific features varied across models. In general, the combination of total scale score, subscale scores and responses to individual questions on a severity measure, the Liebowitz Social Anxiety Scale (LSAS), was the most informative in achieving the highest predictions that alone accounted for about 26% of the variance in treatment outcome. Demographic information, psychiatric history, personality measures, other self-reported or clinician-reported questionnaires, and clinical scales related to anxiety, depression, and quality of life provided no additional predictive power. These findings indicate that combining scaled and individual responses to LSAS questions are informative for predicting individual response to CBT in patients with SAD.https://doi.org/10.1371/journal.pone.0313351 |
| spellingShingle | Qasim Bukhari David Rosenfield Stefan G Hofmann John D E Gabrieli Satrajit S Ghosh Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. PLoS ONE |
| title | Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. |
| title_full | Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. |
| title_fullStr | Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. |
| title_full_unstemmed | Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. |
| title_short | Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach. |
| title_sort | predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics psychiatric history and scales a machine learning approach |
| url | https://doi.org/10.1371/journal.pone.0313351 |
| work_keys_str_mv | AT qasimbukhari predictingtreatmentresponsetocognitivebehaviortherapyinsocialanxietydisorderonthebasisofdemographicspsychiatrichistoryandscalesamachinelearningapproach AT davidrosenfield predictingtreatmentresponsetocognitivebehaviortherapyinsocialanxietydisorderonthebasisofdemographicspsychiatrichistoryandscalesamachinelearningapproach AT stefanghofmann predictingtreatmentresponsetocognitivebehaviortherapyinsocialanxietydisorderonthebasisofdemographicspsychiatrichistoryandscalesamachinelearningapproach AT johndegabrieli predictingtreatmentresponsetocognitivebehaviortherapyinsocialanxietydisorderonthebasisofdemographicspsychiatrichistoryandscalesamachinelearningapproach AT satrajitsghosh predictingtreatmentresponsetocognitivebehaviortherapyinsocialanxietydisorderonthebasisofdemographicspsychiatrichistoryandscalesamachinelearningapproach |