Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391223 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2015.00379 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees.
Water Sci Technol
December 2024
Department of Civil Engineering, National Institute of Technology Kurukshetra, Haryana 136119, India.
This study optimizes standard oxygen transfer efficiency (SOTE) in Venturi flumes investigating the impact of key parameters such as discharge per unit width (), throat width (), throat length (), upstream entrance width (), and gauge readings ( and ). To achieve this, a comprehensive experimental dataset was analyzed using multiple linear regression (MLR), multiple nonlinear regression (MNLR), gradient boosting machine (GBM), extreme gradient boosting (XRT), random forest (RF), M5 (pruned and unpruned), random tree (RT), and reduced error pruning (REP). Model performance was evaluated based on key metrics: correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE).
View Article and Find Full Text PDFAssessment of meteorological factors and air pollution impact on cardiovascular mortality using random forest analysis 2017 to 2020.
Sci Rep
December 2024
Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
Air pollution, a global health hazard, significantly impacts mortality, cardiovascular health, mental well-being, and overall human health. This study aimed to investigate the impact of air pollution and meteorological factors on cardiovascular mortality rates in Mashhad City, northeastern Iran in 2017-2020. We utilized a Random Forest (RF) model in this study.
View Article and Find Full Text PDFWater quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model.
Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters.
View Article and Find Full Text PDFEconomic evaluation of tenofovir disoproxil fumarate prophylaxis to prevent mother-to-child transmission of Hepatitis B virus infection: evidence from a lower-middle income country.
BMC Health Serv Res
December 2024
Mahidol University Health Technology Assessment (MUHTA) Graduate Program, Mahidol University, Bangkok, 10400, Thailand.
No cost-effectiveness information of preventive strategies for mother-to-child transmission (MTCT) of hepatitis B virus (HBV) has existed for policy decision making. This study aimed to compare the cost-effectiveness of alternative strategies to prevent MTCT of HBV in Vietnam. Cost-utility analysis using a hybrid decision-tree and Markov model were performed from healthcare system and societal perspectives.
View Article and Find Full Text PDFClustering and classification for dry bean feature imbalanced data.
Sci Rep
December 2024
School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!