Influencing Factors and Machine Learning-Based Prediction of Side Effects in Psychotherapy
Bibliographic Data
| ID | 15518221 |
|---|---|
| Authors | Lijun Yao (0000-0002-7491-0045, Tongji University), Xudong Zhao (0000-0003-1493-3517, Tongji University), Zhiwei Xu (0000-0002-8630-1204, Fudan University), Yang Chen (0000-0001-5943-3247, Fudan University), Chen Yang (0000-0002-4801-4036), Liang Liu (0000-0003-1372-4596, Tongji University), Qiang Feng (0000-0002-4055-226X, Shanghai East Hospital), Fazhan Chen (0000-0002-3963-1868, Tongji University, corresponding author) |
| Year | 2020 |
| Volume | 11 |
| Pages | 537442-537442 |
| Publication date | 2020-12-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2020.537442 |
| PMID | 33343404 |
| OpenAlex | W3108146711 |
| Language | EN |
| Citations received | 2 |
| References cited | 35 |
Background: Side effects in psychotherapy are a common phenomenon, but due to insufficient understanding of the relevant predictors of side effects in psychotherapy, many psychotherapists or clinicians fail to identify and manage these side effects. The purpose of this study was to predict whether clients or patients would experience side effects in psychotherapy by machine learning and to analyze the related influencing factors. Methods: A self-compiled "Psychotherapy Side Effects Questionnaire (PSEQ)" was delivered online by a WeChat official account. Three hundred and seventy participants were included in the cross-sectional analysis. Psychotherapy outcomes were classified as participants with side effects and without side effects. A number of features were selected to distinguish participants with different psychotherapy outcomes. Six machine learning-based algorithms were then chosen and trained by our dataset to build outcome prediction classifiers. Results: Our study showed that: (1) the most common side effects were negative emotions in psychotherapy, such as anxiety, tension, sadness, and anger, etc. (24.6%, 91/370); (2) the mental state of the psychotherapist, as perceived by the participant during psychotherapy, was the most relevant feature to predict whether clients would experience side effects in psychotherapy; (3) a Random Forest-based machine learning classifier offered the best prediction performance of the psychotherapy outcomes, with an F1-score of 0.797 and an AUC value of 0.804. These numbers indicate a high prediction performance, which allowed our approach to be used in practice. Conclusions: Our Random Forest-based machine learning classifier could accurately predict the possible outcome of a client in psychotherapy. Our study sheds light on the influencing factors of the side effects of psychotherapy and could help psychotherapists better predict the outcomes of psychotherapy
Anger · Anxiety · Machine learning · Psychiatry · Psychotherapist · Random forest · Sadness · Clinical Psychology · Computer Science · Mental Health Research Topics · Personality Disorders and Psychopathology · Psychology · Psychotherapy Techniques and Applications · Artificial Intelligence
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| Unique citing works | 2 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2022 - 2023 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |