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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

150
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
150

You might also read

Related Articles

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

Sort by
Same author

Relevance of prematurity and foetal growth restriction for romantic relationships, health-risk behaviours, and socio-economic outcomes in adulthood.

European journal of public health·2026
Same author

Zeitschrift fur Psychosomatische Medizin und Psychotherapie·2026
Same author

Psychometric evaluation of the Questionnaire of Life Satisfaction (FLZ<sup>M</sup>) in a representative population sample.

BMC psychology·2026
Same author

Psychometric Properties of the Perceived Control Short Scale (PCSS-6): Reliability, Factor Structure, and Measurement Invariance in a Representative Population Sample.

Psychotherapie, Psychosomatik, medizinische Psychologie·2026
Same author

The Impact of Previous Suicide Attempts on Psychotherapy Outcome: A Multiverse Approach to Treatment Success Considering Clinical Severity in Routine Clinical Data.

Archives of suicide research : official journal of the International Academy for Suicide Research·2026
Same author

Global and regional DNA methylation patterns in heart failure: a case-control analysis.

EBioMedicine·2026

Related Experiment Video

Updated: Jul 8, 2025

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

8.3K

Predicting treatment response using machine learning: A registered report.

Kristin Jankowsky1, Lina Krakau2, Ulrich Schroeders1

  • 1Psychological Assessment, University of Kassel, Kassel, Germany.

The British Journal of Clinical Psychology
|December 19, 2023
PubMed
Summary

Predicting psychotherapy treatment response in inpatients is possible using baseline data. Machine learning models, particularly those using treatment-related and psychological indicators, significantly improve prediction accuracy over traditional methods.

Keywords:
inpatientsmachine learningpredictive modellingprognostic markerstreatment response

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jul 8, 2025

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

8.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Psychiatry and Mental Health
  • Machine Learning in Healthcare
  • Clinical Psychology

Background:

  • Psychotherapy treatment response research often lacks ecological validity due to focus on outpatients or clinical trials.
  • Accurate prediction of treatment response in naturalistic inpatient samples is crucial for reducing treatment failures and identifying at-risk patients.
  • Understanding predictors of treatment success in inpatient settings is needed for improved patient care and treatment evaluation.

Purpose of the Study:

  • To compare the predictive performance of various machine learning algorithms for psychotherapy treatment response in a naturalistic inpatient sample.
  • To identify the unique and joint contributions of demographics, physical indicators, psychological indicators, and treatment-related variables to treatment success.
  • To enhance the accuracy of predicting treatment outcomes in inpatient mental health settings.

Main Methods:

  • A naturalistic inpatient sample (N=723) was analyzed.
  • Treatment response was operationalized as a significant reduction in symptom severity (Patient Health Questionnaire Anxiety and Depression Scale).
  • Machine learning algorithms and linear regressions were compared using demographic, physical, psychological, and treatment-related variables.

Main Results:

  • Baseline symptom severity strongly correlated with post-treatment severity (R²=.32).
  • Machine learning algorithms outperformed linear regressions, increasing predictive performance by R²=.12 when all variables were used.
  • Treatment-related variables were the most predictive, followed by psychological indicators; physical indicators and demographics had negligible predictive value.

Conclusions:

  • Treatment response in naturalistic inpatient settings is predictable using baseline indicators.
  • Machine learning algorithms, particularly with regularization, offer superior predictive performance compared to models including nonlinear and interaction effects.
  • Heterogeneous aspects of mental health possess incremental predictive value and should be incorporated as prognostic markers in treatment modeling.