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

6.2K
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...
6.2K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.8K
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...
4.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

14
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
14
Law of Effect01:06

Law of Effect

1.3K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Overview of the linking exercise for advancing daily stress (LEADS) management and resilience randomized controlled trial in African American families.

Contemporary clinical trials·2026
Same author

A randomized controlled Phase I de-escalation trial of molnupiravir and nirmatrelvir/ritonavir combination for mild-moderate SARS-CoV-2 infection.

The Journal of antimicrobial chemotherapy·2026
Same author

Associations of dietary diversity with type 2 diabetes in two Chinese cohorts: a multi-omics study.

Food & function·2026
Same author

Optimal Dose and Safety of Intravenous Favipiravir in Hospitalized Patients With COVID-19: A Dose-Escalating, Randomized Controlled Phase Ib Study.

Clinical pharmacology and therapeutics·2026
Same author

Paired and AB/BA Cross-Over Design in Early Phase Clinical Trials: A Closer Look at Within-Subject Variance Bias.

Pharmaceutical statistics·2026
Same author

Factors associated with inadequate sleep among high school students in New Mexico: Data from the New Mexico Youth Risk and Resiliency Survey 2015-2023.

Sleep medicine·2026
Same journal

Report on "Axiomatizing Conditional Normative Reasoning".

Kunstliche intelligenz·2024
Same journal

In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies.

Kunstliche intelligenz·2023
Same journal

Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs.

Kunstliche intelligenz·2023
Same journal

A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment.

Kunstliche intelligenz·2022
Same journal

Some Thoughts on AI Stimulated by Michael Wooldridge's Book "The Road to Conscious Machines. The Story of AI".

Kunstliche intelligenz·2022
Same journal

Simplifying Programming for Non-technical Students: A Hermeneutic Approach.

Kunstliche intelligenz·2022
See all related articles

Related Experiment Video

Updated: May 12, 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

8.2K

Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial

Thomas Jaki1,2, Chi Chang3, Alena Kuhlemeier4

  • 1University of Regensburg, Bajuwarenstraße 4, 93055 Regenburg, Germany.

Kunstliche Intelligenz
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

Predicting individual treatment effects using machine learning (ML) and artificial intelligence (AI) can personalize medicine. This approach aims to match the right treatment to the right patient for improved outcomes.

Keywords:
BARTHeterogeneity in treatment effectsPersonalized medicinePredicted individual treatment effects

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

7.4K
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

6.6K

Related Experiment Videos

Last Updated: May 12, 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

8.2K
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

7.4K
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

6.6K

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Clinical Decision Support

Background:

  • Personalized medicine aims to tailor treatments to individual patients for optimal efficacy.
  • Predicting patient-specific treatment responses is crucial for advancing healthcare.
  • Current methods often lack the precision to identify individual treatment benefits.

Purpose of the Study:

  • To demonstrate the potential of Machine Learning (ML) and Artificial Intelligence (AI) in predicting individual treatment effects (ITE).
  • To introduce and illustrate the Predicted Individual Treatment Effects (PITE) framework.
  • To highlight research opportunities and challenges in predicting ITE.

Main Methods:

  • Utilized baseline covariates (features) within the PITE framework.
  • Employed ML and AI methodologies to predict treatment benefit for individual patients.
  • Compared predicted treatment effects against alternative interventions.

Main Results:

  • Illustrated the feasibility of using ML/AI for predicting ITE.
  • Demonstrated the PITE framework's capability in identifying potential treatment benefits.
  • Provided a foundation for further research in personalized treatment prediction.

Conclusions:

  • ML and AI hold significant promise for predicting individual treatment effects.
  • The PITE framework offers a viable approach for personalized medicine.
  • Further research is needed to address open challenges and refine ITE prediction methods.