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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

184
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
184
Associative Learning01:27

Associative Learning

452
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
452
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

97
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...
97
Observational Learning01:12

Observational Learning

213
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
213
Introduction to Learning01:18

Introduction to Learning

478
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
478

You might also read

Related Articles

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

Sort by
Same author

A Novel Hybrid Ordinal Learning Model with Health Care Application.

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society·2025
Same author

Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection.

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society·2025
Same author

A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society·2025
Same author

PLASMA-CYCLEGAN: PLASMA BIOMARKER-GUIDED MRI TO PET CROSS-MODALITY TRANSLATION USING CONDITIONAL CYCLEGAN.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.

IISE transactions on healthcare systems engineering·2024
Same author

Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans.

Journal of endodontics·2024
Same journal

Multi-modal mixed-type structural equation modeling with structured sparsity for subgroup discovery from heterogeneous health data.

IISE transactions·2025
Same journal

Ranking and Combining Latent Structured Predictive Scores without Labeled Data.

IISE transactions·2025
Same journal

Discriminant Subgraph Learning from Functional Brain Sensory Data.

IISE transactions·2023
Same journal

Access Planning and Resource Coordination for Clinical Research Operations.

IISE transactions·2020
See all related articles

Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K

A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.

Xiaonan Liu1, Kewei Chen2, David Weidman2

  • 1Industrial Engineering, Arizona State University, Tempe, AZ, USA.

IISE Transactions
|July 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Incomplete-Multimodality Transfer Learning (IMTL) to effectively use incomplete datasets for predictive analytics. IMTL improves diagnostic accuracy, particularly for early Alzheimer's detection using imaging data.

Keywords:
Health Careincomplete multimodality datapredictive analyticstransfer learning

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
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

Related Experiment Videos

Last Updated: Jul 24, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
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

Area of Science:

  • Machine Learning
  • Medical Informatics
  • Data Science

Background:

  • Multimodality datasets offer complementary information but often have missing data, creating Incomplete Multimodality Datasets (IMD).
  • Fusing incomplete multimodality data presents a significant challenge for predictive analytics.
  • Existing methods struggle with universally unavailable modalities due to cost and accessibility.

Purpose of the Study:

  • To propose a novel Incomplete-Multimodality Transfer Learning (IMTL) model for predictive analytics on IMDs.
  • To enable transfer learning across sub-cohorts with varying missing modality patterns.
  • To enhance diagnostic and prognostic accuracy for diseases like early-stage Alzheimer's Disease (AD).

Main Methods:

  • Developed an Incomplete-Multimodality Transfer Learning (IMTL) model.
  • Utilized an Expectation-Maximization (EM) algorithm for parameter estimation.
  • Extended IMTL to a collaborative learning paradigm for privacy preservation.

Main Results:

  • IMTL demonstrated the ability for out-of-sample prediction.
  • A theoretical guarantee for larger Fisher information compared to non-transfer learning models was proven.
  • Applied to Mild Cognitive Impairment (MCI) diagnosis and prognosis using incomplete imaging data, IMTL achieved higher accuracy.

Conclusions:

  • IMTL effectively handles incomplete multimodality datasets for improved predictive modeling.
  • The model offers theoretical advantages and practical improvements in accuracy for medical applications.
  • IMTL shows promise for early diagnosis and prognosis, especially in healthcare with privacy concerns.