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

Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Integrated Healthcare System

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...

Models of Health Promotion and Illness Prevention II

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...

Models of Health Promotion and Illness Prevention I

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

You might also read

Related Articles

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

Sort by

Same author

Sensors for Digital Transformation in Smart Forestry.

Sensors (Basel, Switzerland)·2024

Same author

The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

Health and technology·2024

Same author

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Bioinformatics (Oxford, England)·2023

Same author

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

Journal of medical Internet research·2023

Same author

Explainability and causability in digital pathology.

The journal of pathology. Clinical research·2023

Same author

Explainable AI and Multi-Modal Causability in Medicine.

I-com·2023

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Andreas Holzinger^1,2

¹Research Unit, HCI-KDD, Institute for Medical Informatics, Statistics & Documentation, Medical University Graz, Graz, Austria. a.holzinger@hci-kdd.org.

Brain Informatics

|October 18, 2016

Summary

This summary is machine-generated.

Interactive machine learning (iML) addresses health data challenges where automatic approaches fail due to limited data. Human-in-the-loop iML optimizes learning by leveraging expert input for complex problems.

Keywords:

Health informatics Interactive machine learning

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Feb 20, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Computer Science
Health Informatics

Background:

Machine learning (ML) is rapidly advancing in computer science.
Health informatics presents significant challenges for ML, particularly with limited data or rare events.
Automatic machine learning (aML) requires large datasets, which are often unavailable in healthcare.

Purpose of the Study:

To define and explore interactive machine learning (iML) as a solution for health informatics.
To highlight the benefits of human-in-the-loop systems in ML for complex computational problems.

Main Methods:

Defining interactive machine learning (iML) as algorithms that learn through interaction with agents, including humans.
Utilizing human expertise within the learning phase to guide ML algorithms.
Applying iML to computationally hard problems like subspace clustering and k-anonymization of health data.

Main Results:

iML can overcome limitations of aML when dealing with small datasets or rare events in health informatics.
Human-in-the-loop approaches can significantly reduce the complexity of NP-hard problems.
Expert input aids in heuristic sample selection, improving learning efficiency.

Conclusions:

Interactive machine learning offers a promising approach for tackling complex challenges in health informatics.
The integration of human expertise into ML algorithms is crucial for domains with data scarcity.
iML enhances the capability of ML to solve problems that are computationally intractable for aML alone.