Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

56
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
56
Elements Crucial for Effective Psychotherapy01:25

Elements Crucial for Effective Psychotherapy

34
Research has highlighted several critical factors that influence the effectiveness of psychotherapy, such as the therapeutic alliance, the therapist, and the client.
The Therapeutic Alliance
The therapeutic alliance refers to the relationship between the therapist and the client. The alliance strengthens when the therapist and the client engage in a nurturing, supportive, trusting, empathetic, and respectful relationship, improving therapeutic outcomes. Therapists must monitor this relationship...
34
Treatment Strategies for Psychological Disorders01:24

Treatment Strategies for Psychological Disorders

100
Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
Psychological therapies focus on modifying emotions, thoughts, and behaviors through talking, interpreting, listening, rewarding, challenging, and modeling. Clinical psychologists, counselors, and social workers commonly practice psychotherapy. Clinical...
100
Clinical Trials: Overview01:11

Clinical Trials: Overview

2.9K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
2.9K
Data Validation01:03

Data Validation

5.0K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.0K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

509
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
509

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Review: Internet-based cognitive behavioral therapy for children and adolescents with anxiety disorders - a meta-analysis of efficacy and impact of age and support.

Child and adolescent mental health·2026
Same author

Forecasting stress transitions using ecological momentary assessment data and machine learning.

Internet interventions·2026
Same author

Measurement stability and differential domain-specific trajectories of the PHQ-9 in late-life depression: Evidence from a collaborative care trial.

Journal of affective disorders·2026
Same author

Standardized Methods for Evaluating Physical and Eating Behaviors: The WEALTH Cross-Sectional Study Protocol.

JMIR research protocols·2026
Same author

Personalized PHQ-9 test length using probability density estimation based on conditional probability and K-Nearest Neighbours.

Internet interventions·2026
Same author

Effectiveness of Guided Internet-Based Cognitive Behavioral Therapy for Adult Anxiety and Depression in Routine Care: An Observational Study.

International journal of methods in psychiatric research·2026

Related Experiment Video

Updated: Jun 12, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

Choosing the right treatment - combining clinicians' expert knowledge with data-driven predictions.

Eduardo Maekawa1,2, Esben Jensen3,4, Pepijn van de Ven1,2

  • 1Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland.

Frontiers in Psychiatry
|September 18, 2024
PubMed
Summary

A hybrid Bayesian network model improves mental disorder treatment selection. The model, combining data and expert knowledge, accurately identifies the most suitable treatment probabilities for conditions like depression and phobias.

Keywords:
Bayesian networkcliniciandigital psychiatrymachine learningmental disorders

More Related Videos

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: Jun 12, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Psychiatry

Background:

  • Mental health treatment selection relies on accurate diagnosis.
  • Data-driven approaches can enhance clinical decision-making.
  • Bayesian networks offer a probabilistic framework for complex diagnostic tasks.

Purpose of the Study:

  • To develop a Bayesian network model for aiding mental health specialists in selecting treatments.
  • To compare a purely data-driven model with a hybrid model incorporating expert knowledge.
  • To assess the model's effectiveness in identifying suitable treatments for Depression, Panic Disorder, Social Phobia, and Specific Phobia.

Main Methods:

  • A dataset of 1,094 individuals from Denmark was used, including socio-demographic and mental health information.
  • A Bayesian network was initially trained on data alone, then refined with expert input (hybrid model).
  • The model generated probabilities for each mental disorder, indicating treatment suitability.

Main Results:

  • The hybrid model outperformed the data-driven approach, achieving an AUC of 0.85 versus 0.80.
  • In 90.1% of cases, the hybrid model ranked the actual treated disorder as the highest (67.3%) or second-highest (22.8%) probability.
  • Analysis of discrepancies suggested potential comorbid disorders in some cases.

Conclusions:

  • Incorporating expert knowledge significantly enhances the performance of Bayesian network models for treatment selection.
  • The hybrid model provides probabilistic outputs for multiple disorders, supporting collaborative treatment decisions.
  • This approach offers a valuable data-driven tool for mental health professionals and patients.