Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

Data Validation

Data Validation

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...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

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

Sort by

Same author

Social jetlag elicits fatty liver via perturbed circulating prolactin rhythm-mediated circadian remodeling of hepatic lipid metabolism.

Military Medical Research·2025

Same author

Medical Artificial Intelligence and Human Values.

The New England journal of medicine·2024

Same author

Artificial Intelligence in Medicine.

The New England journal of medicine·2023

Same author

Subdomain Adaptation With Manifolds Discrepancy Alignment.

IEEE transactions on cybernetics·2021

Same author

Modeling Multi-View Dependence in Bayesian Networks for Alzheimer's Disease Detection.

Studies in health technology and informatics·2019

Same author

Microvolt T-wave alternans complemented with electrophysiologic study for prediction of ventricular tachyarrhythmias in patients with arrhythmogenic right ventricular cardiomyopathy: a long-term follow-up study.

Chinese medical journal·2019

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

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

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

Published on: January 8, 2020

A model driven approach to imbalanced data sampling in medical decision making.

Hong-Li Yin¹, Tze-Yun Leong

¹Medical Computing Lab, School of Computing, National University of Singapore, Singapore. yinhl@comp.nus.edu.sg

Studies in Health Technology and Informatics

|September 16, 2010

Summary

This study introduces a novel model-driven sampling method to balance imbalanced medical data, crucial for accurate classification and reducing misclassification costs in healthcare. The new approach shows comparable performance to existing methods.

Related Experiment Videos

Last Updated: Jun 8, 2026

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

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

Published on: January 8, 2020

Area of Science:

Medical Informatics
Machine Learning
Data Science

Background:

Imbalanced data distribution significantly impacts medical classification accuracy.
Misclassifying minority classes in medical decision support carries high costs.

Purpose of the Study:

To propose a new model-driven sampling approach for balancing imbalanced medical data.
To address the limitations of existing data sampling methods in medical applications.

Main Methods:

Developed a generative sampling technique using a probabilistic graphical model.
Compared the proposed approach with two representative imbalanced data sampling methods.
Evaluated performance on four diverse medical datasets.

Main Results:

The model-driven sampling approach demonstrated performance comparable to existing methods.
Empirical study identified challenges specific to imbalanced medical data.
Highlighted strengths and limitations of various sampling techniques.

Conclusions:

The proposed model-driven sampling method is a viable approach for imbalanced medical data.
Incorporating domain knowledge could further enhance the performance of these methods.