A Machine Learning-based Approach to Identify Therapists who can Perceive Client-Side Effects in Psychotherapy

Abstract Background: Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Methods: We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories.Results: Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common type was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category.Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

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Prediction of the Side Effects Perceived by Therapists in Psychotherapy (Preprint)

10.2196/preprints.29338 ◽

2021 ◽

Author(s):

Lijun Yao ◽

Zhiwei Xu ◽

Xudong Zhao ◽

Yang Chen ◽

Liang Liu ◽

...

Keyword(s):

Machine Learning ◽

Side Effects ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Cross Sectional ◽

Psychological States ◽

Learning Classifier ◽

Psychological Activity ◽

Client Side ◽

Sectional Analysis

BACKGROUND Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. OBJECTIVE Using machine learning techniques to distinguish therapists with and without the perception of consulting side effects, and identify the predictive factors of therapists who could perceive client side effects in psychotherapy. METHODS We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. RESULTS Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category. CONCLUSIONS Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

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

Frontiers in Psychiatry ◽

10.3389/fpsyt.2020.537442 ◽

2020 ◽

Vol 11 ◽

Author(s):

Lijun Yao ◽

Xudong Zhao ◽

Zhiwei Xu ◽

Yang Chen ◽

Liang Liu ◽

...

Keyword(s):

Machine Learning ◽

Side Effects ◽

Random Forest ◽

Influencing Factors ◽

Prediction Performance ◽

Psychotherapy Outcomes ◽

Cross Sectional ◽

Learning Classifier ◽

Auc Value ◽

Sectional Analysis

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.

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ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.599 ◽

2019 ◽

Vol 15 (2) ◽

pp. 141-148

Author(s):

Sri Rahayu ◽

Fitra Septia Nugraha ◽

Muhammad Ja’far Shidiq

Keyword(s):

Neural Network ◽

Machine Learning ◽

Side Effects ◽

20Th Century ◽

High Accuracy ◽

Machine Learning Method ◽

Learning Method ◽

Good Classification ◽

Auc Value

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

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Self-reported medication side effects in an older cohort living independently in the community - the Melbourne Longitudinal Study on Healthy Ageing (MELSHA): cross-sectional analysis of prevalence and risk factors

BMC Geriatrics ◽

10.1186/1471-2318-10-37 ◽

2010 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Jennifer A Thomson ◽

Wei C Wang ◽

Colette Browning ◽

Hal L Kendig

Keyword(s):

Risk Factors ◽

Longitudinal Study ◽

Side Effects ◽

Healthy Ageing ◽

Cross Sectional ◽

Medication Side Effects ◽

Cross Sectional Analysis ◽

Medication Side ◽

Sectional Analysis

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Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study

10.1101/2020.02.29.20029603 ◽

2020 ◽

Cited By ~ 15

Author(s):

Xiaolong Qi ◽

Zicheng Jiang ◽

Qian Yu ◽

Chuxiao Shao ◽

Hongguang Zhang ◽

...

Keyword(s):

Machine Learning ◽

Hospital Stay ◽

Sensitivity And Specificity ◽

Final Analysis ◽

Predictive Performance ◽

Test Machine ◽

Cross Sectional ◽

Test Dataset ◽

Lung Lobe

AbstractObjectivesTo develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.DesignCross-sectionalSettingMulticenterParticipantsA total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis.InterventionCT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level.Main outcomesShort-term hospital stay (≤10 days) and long-term hospital stay (>10 days).ResultsThe CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset.ConclusionsThe machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.

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Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects

Vaccines ◽

10.3390/vaccines9060556 ◽

2021 ◽

Vol 9 (6) ◽

pp. 556

Author(s):

Ma’mon M. Hatmal ◽

Mohammad A. I. Al-Hatamleh ◽

Amin N. Olaimat ◽

Malik Hatmal ◽

Dina M. Alhaj-Qasem ◽

...

Keyword(s):

Machine Learning ◽

Side Effects ◽

Cross Sectional Study ◽

Vaccine Hesitancy ◽

Medical Attention ◽

Gradient Boosting ◽

Sectional Study ◽

Cross Sectional ◽

Post Vaccination ◽

Extreme Gradient Boosting

Background: Since the coronavirus disease 2019 (COVID-19) was declared a pandemic, there was no doubt that vaccination is the ideal protocol to tackle it. Within a year, a few COVID-19 vaccines have been developed and authorized. This unparalleled initiative in developing vaccines created many uncertainties looming around the efficacy and safety of these vaccines. This study aimed to assess the side effects and perceptions following COVID-19 vaccination in Jordan. Methods: A cross-sectional study was conducted by distributing an online survey targeted toward Jordan inhabitants who received any COVID-19 vaccines. Data were statistically analyzed and certain machine learning (ML) tools, including multilayer perceptron (MLP), eXtreme gradient boosting (XGBoost), random forest (RF), and K-star were used to predict the severity of side effects. Results: A total of 2213 participants were involved in the study after receiving Sinopharm, AstraZeneca, Pfizer-BioNTech, and other vaccines (38.2%, 31%, 27.3%, and 3.5%, respectively). Generally, most of the post-vaccination side effects were common and non-life-threatening (e.g., fatigue, chills, dizziness, fever, headache, joint pain, and myalgia). Only 10% of participants suffered from severe side effects; while 39% and 21% of participants had moderate and mild side effects, respectively. Despite the substantial variations between these vaccines in the presence and severity of side effects, the statistical analysis indicated that these vaccines might provide the same protection against COVID-19 infection. Finally, around 52.9% of participants suffered before vaccination from vaccine hesitancy and anxiety; while after vaccination, 95.5% of participants have advised others to get vaccinated, 80% felt more reassured, and 67% believed that COVID-19 vaccines are safe in the long term. Furthermore, based on the type of vaccine, demographic data, and side effects, the RF, XGBoost, and MLP gave both high accuracies (0.80, 0.79, and 0.70, respectively) and Cohen’s kappa values (0.71, 0.70, and 0.56, respectively). Conclusions: The present study confirmed that the authorized COVID-19 vaccines are safe and getting vaccinated makes people more reassured. Most of the post-vaccination side effects are mild to moderate, which are signs that body’s immune system is building protection. ML can also be used to predict the severity of side effects based on the input data; predicted severe cases may require more medical attention or even hospitalization.

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Scalable density based spatial clustering with integrated one-class SVM for noise reduction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.9.10093 ◽

2018 ◽

Vol 7 (2.22) ◽

pp. 28

Author(s):

K. Nafees Ahmed ◽

T. Abdul Razak

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Side Effects ◽

Information Extraction ◽

Noise Reduction ◽

Spatial Clustering ◽

Machine Learning Technique ◽

Learning Classifier ◽

Learning Technique ◽

High Scalability

Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class SVM, a machine learning technique for noise reduction, a modified variant of DBSCAN called NRDBSCAN. Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. Further, the proposed technique is parallelized using Spark architecture, thereby increasing its scalability and its ability to handle large amounts of data. Experiments and comparisons with similar techniques indicate high scalability levels and high homogeneity levels in the clustering process.

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