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

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.

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

2021 ◽  
Author(s):  
Lijun Yao ◽  
Zhiwei Xu ◽  
Xudong Zhao ◽  
Yang Chen ◽  
Liang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Predictive Performance ◽  
Cross Sectional ◽  
Psychological States ◽  
Learning Classifier ◽  
Auc Value ◽  
Psychological Activity ◽  
Client Side ◽  
Sectional Analysis

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.

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

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.

scholarly journals Nursing students' attitudes, knowledge, and willingness to receive the COVID-19 vaccine: A cross-sectional study

2021 ◽  
Author(s):  
Ning Jiang ◽  
Baojian Wei ◽  
Hua Lin ◽  
Youjuan Wang ◽  
Shouxia Chai ◽  
...  
Keyword(s):  
Nursing Students ◽  
Side Effects ◽  
Influencing Factors ◽  
Family Members ◽  
Cross Sectional Survey ◽  
School Students ◽  
Cross Sectional ◽  
Vaccine Acceptance ◽  
Academic Background ◽  
To Receive

Aim: To investigate nursing students' konwledge, attitudes and willingness to receive the COVID-19 vaccine, and the influencing factors. Background: Vaccination is one of the effective measures to prevent COVID-19, but the vaccination acceptance varies across countries and populations. As reserve nurses, nursing students have both the professionalism of medical personnel and the special characteristics of school students, their attitudes, knowledge, and willingness to receive the COVID-19 vaccine may greatly affect the vaccine acceptance of the population now and in the future. But little research has been done on vaccine acceptance among nursing students. Design: A cross-sectional survey of nursing students was conducted via online questionnaires in March 2021. Methods: Descriptive statistics, independent sample t tests/one-way ANOVA (normal distribution), Mann-Whitney U tests/Kruskal-Wallis H tests (skewness distribution) and multivariate linear regression were performed. Results: The score rate of attitude, knowledge and vaccination willingness were 70.07%, 80.70% and 84.38% respectively. Attitude was significantly influenced by family economic conditions and whether a family member had been vaccinated. The main factors influencing knowledge were gender, grade and academic background. In terms of willingness, gender, academic background, visits to risk areas, whether family members were vaccinated, and whether they had side effects were significant influencing factors. Conclusions: The vaccine acceptance of nursing students was fair. Greater focus needed to be placed on the males, those of younger age, with a science background, and having low grades, as well as on students whose family members had not received the COVID-19 vaccine or had side effects from the vaccine. Targeted intervention strategies were recommended to improve vaccination rates.

Accurate classification of pediatric colonic IBD subtype using a random forest machine learning classifier

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jasbir Dhaliwal ◽  
Lauren Erdman ◽  
Erik Drysdal ◽  
Firas Rinawi ◽  
Jennifer Muir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Classifier

scholarly journals Forest Fire Prediction using Machine Learning Models based on DC, Wind and RH

2020 ◽  
Vol 8 (6S) ◽  
pp. 142-143
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Forest Fire ◽  
Random Forest Algorithm ◽  
Learning Models ◽  
Learning Classifier ◽  
Machine Learning Models ◽  
Classifier Algorithms

The paper points out forest fire prediction using machine learning models on the basis of viz. DC, Wind, RH out of the several machine learning classifier algorithms, It is relevant that random forest algorithm generates optimum accuracy(99.61%).

scholarly journals The pneumonia severity index: assessment and comparison to popular machine learning classifiers

2021 ◽  
Author(s):  
Dawei Wang ◽  
Deanna R. Willis ◽  
Yuehwern Yih
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Pneumonia Severity Index ◽  
Severity Index ◽  
Community Acquired Pneumonia ◽  
Prediction Performance ◽  
Pneumonia Severity ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Accuracy Performance

AbstractPneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). Machine learning classifiers, such as Random Forest, provided a significant improvement (∼29% in PR AUC and ∼5% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). There were also statistically significant differences (p<0.05) between Random Forest and PSI among various races/ethnicities. Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.Key MessagesThis work compared the prognostication accuracy performance of patient severity with Community Acquired Pneumonia (CAP) between Pneumonia Severity Index (PSI) and nine machine learning classifiers and found machine learning classifiers provided a significant improvement.

scholarly journals ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

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.

scholarly journals Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

2021 ◽  
Vol 10 (18) ◽  
pp. 4281
Author(s):  
Gracia Castro-Luna ◽  
Diana Jiménez-Rodríguez ◽  
Ana Belén Castaño-Fernández ◽  
Antonio Pérez-Rueda
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Corneal Thickness ◽  
Cross Sectional Study ◽  
Machine Learning Techniques ◽  
Critical Determinant ◽  
Cross Sectional ◽  
Essential Variable ◽  
Learning Techniques ◽  
Initial Stage

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

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