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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

155
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...
155
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

271
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
271
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
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...
6.6K
Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

77
Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
77
Weighted Mean00:57

Weighted Mean

6.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.0K
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

79
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
79

You might also read

Related Articles

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

Sort by
Same author

A monitoring-modeling approach to SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup> secondary conversion ratio estimation during haze periods in Beijing, China.

Journal of environmental sciences (China)·2019
Same author

Short Versus Long Cephalomedullary Nails for Fixation of Stable Versus Unstable Intertrochanteric Femur Fractures at a Level 1 Trauma Center.

Orthopedics·2019
Same author

E-cadherin is Required for the Homeostasis of Lgr5<sup>+</sup> Gastric Antral Stem Cells.

International journal of biological sciences·2019
Same author

Development of a real-time nucleic acid sequence-based amplification assay for the rapid detection of Salmonella spp. from food.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]·2019
Same author

Conjugated Microporous Polymers with Tunable Electronic Structure for High-Performance Potassium-Ion Batteries.

ACS nano·2019
Same author

Acid Suppression to Prevent Gastrointestinal Bleeding in Patients With Ventricular Assist Devices.

The Journal of surgical research·2018

Related Experiment Video

Updated: Nov 26, 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

8.5K

Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules.

Chong Zhang1, Jingxiang Chen1, Haoda Fu2

  • 1University of North Carolina at Chapel Hill.

Statistica Sinica
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

Precision medicine aims to tailor therapies using individual characteristics. A new method, Multicategory Outcome weighted Margin-based Learning (MOML), effectively estimates optimal treatment rules for multiple treatment options, improving patient outcomes.

Keywords:
Angle-based ClassifierLarge-marginMultiple TreatmentsOutcome Weighted LearningPrecision MedicineSupport Vector Machine

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Related Experiment Videos

Last Updated: Nov 26, 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

8.5K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Translational Medicine

Background:

  • Chronic diseases exhibit significant heterogeneity, necessitating personalized treatment approaches.
  • Precision medicine seeks to optimize patient outcomes by tailoring therapies based on individual characteristics.
  • Estimating individual treatment rules (ITRs) is crucial for personalized medicine, especially in complex, multi-treatment scenarios.

Purpose of the Study:

  • To address the limitations of existing methods in estimating ITRs for multiple treatment options.
  • To propose a novel, generalizable learning method, Multicategory Outcome weighted Margin-based Learning (MOML), for multiple treatment scenarios.
  • To evaluate the performance and theoretical properties of the proposed MOML method.

Main Methods:

  • Developed Multicategory Outcome weighted Margin-based Learning (MOML), a generalized approach for estimating ITRs with multiple treatments.
  • Demonstrated Fisher consistency and established convergence rate properties for the MOML estimator.
  • Incorporated sparse l1-penalty for variable selection within the MOML framework.

Main Results:

  • MOML demonstrated competitive performance compared to existing methods in simulated data.
  • The proposed method showed effectiveness in analyzing a type 2 diabetes mellitus observational study.
  • MOML provides a robust framework for personalized medicine with multiple treatment choices.

Conclusions:

  • MOML offers a powerful and generalizable approach for estimating individual treatment rules in multicategory settings.
  • The method advances the field of precision medicine by enabling more effective treatment selection for complex diseases.
  • Further application of MOML can lead to improved clinical decision-making and patient outcomes.