Influencing Factors and Machine Learning-Based Prediction of Side Effects in Psychotherapy

Abstract

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.

Therapists and psychotherapy side effects in China: A machine learning-based study

“…In our study, “made the clients or patients feel bad” (49.8%) was the most common client-side effect reported by therapists, which is consistent with earlier findings ( Bystedt et al., 2014 ; Gerke et al., 2020 ; Schermuly-Haupt et al., 2018 ; Yao et al., 2020 ). Qualitative studies focusing on the therapists' views on the negative effects of psychotherapy showed that the characteristics of negative effects included “short-term negative effects”, “no treatment effect”, “deterioration”, “dependency”, and “impact on other life domains” ( Bystedt et al., 2014 ).…”

“…In psychotherapy, 38.5% of patients with depression (n = 135) exhibited one adverse effect ( Peth et al., 2018 ). Our recent study reported that the incidence of psychotherapy side effects was 31.1% (115/370), and “feel bad in psychotherapy” was the most common side effect (24.6%) ( Yao et al., 2020 ). In group psychotherapy, 43.7% of patients experienced severe and extremely severe side effects or burdens ( Linden et al., 2020 ).…”

“…The treatment preferences included the characteristics of the therapist. In our recent study, the mental state of the psychotherapist was the most crucial feature in predicting whether a client would experience side effects in psychotherapy ( Yao et al., 2020 ). Interestingly, when the psychotherapy outcome was regularly monitored and fed back to the psychotherapist, the deterioration effect of the psychotherapy was significantly reduced ( Lambert et al., 2002 ).…”

“…Their results modestly predicted alliance ratings and suggested ML technologies would help examine alliances in future studies. In one of our recent works, supervised ML was adopted to build a prediction model that can uncover clients who might have consulting side effects in psychotherapy based on information from the psychotherapy ( Yao et al., 2020 ). By comparing six models based on different ML algorithms, the model using the Random Forest algorithm performed the best in predicting psychotherapy outcomes.…”

Sex differences in rTMS treatment response: A deep learning‐based EEG investigation

“…Improved and recurrent neural networks (RNN) both perform optimally when classifying EEG data, but due to size and type of data set, LSTM has been used for optimal results here. Offering a high predictive value, machine learning approaches have been widely used in psychiatry research (Yao et al., 2020 ). Deep learning algorithms for the purposes of classifying psychiatric illnesses and predicting treatment outcomes have yielded successful results in some recent studies (Cosmo et al., 2021 ; de Bardeci et al., 2021 ; Etkin, 2019 ; Zhang et al., 2021a ).…”