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

Ethical Standards I01:25

Ethical Standards I

1.4K
The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
1.4K
Ethical Standards II01:23

Ethical Standards II

1.2K
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
1.2K
Legal Guidelines for Documentation01:06

Legal Guidelines for Documentation

1.9K
The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:
1.9K
Censoring Survival Data01:09

Censoring Survival Data

437
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
437
Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

2.2K
The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
2.2K
Blinding01:11

Blinding

3.7K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
3.7K

You might also read

Related Articles

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

Sort by
Same author

Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.

Frontiers in digital health·2026
Same author

Scaling Electronic Consent for Research Integrated into Clinical Routine Processes.

Studies in health technology and informatics·2026
Same author

Accurate Yet Privacy-Preserving Determination of Case Numbers Across German University Hospital Health Data.

Studies in health technology and informatics·2026
Same author

A FHIR-Based Dashboard as an Integrated Research Patient Record and Quality Assurance Tool.

Studies in health technology and informatics·2026
Same author

Setting up a DataSHIELD Hub for the German Medical Informatics Initiative: Challenges and Lessons Learned.

Studies in health technology and informatics·2026
Same author

Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.

Studies in health technology and informatics·2026
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Dec 17, 2025

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

15.0K

Better Safe than Sorry - Implementing Reliable Health Data Anonymization.

Raffael Bild1, Klaus A Kuhn1, Fabian Prasser2,3

  • 1University hospital rechts der Isar, Technical University of Munich, Germany.

Studies in Health Technology and Informatics
|June 24, 2020
PubMed
Summary
This summary is machine-generated.

Ensuring reliable data anonymization is crucial for privacy in biomedical research. This study introduces a framework using fractional and interval arithmetic to improve the accuracy of privacy models, making anonymization practical with minimal impact on data utility.

Keywords:
anonymizationdata protectionreliable computing

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.9K

Related Experiment Videos

Last Updated: Dec 17, 2025

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

15.0K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.9K

Area of Science:

  • Biomedical Informatics
  • Computer Science
  • Data Privacy

Background:

  • Modern biomedical research generates large datasets, necessitating health data sharing and reuse.
  • Data sharing raises significant privacy concerns, making robust anonymization techniques essential.
  • Current anonymization methods often rely on floating-point approximations, potentially compromising formal privacy guarantees.

Purpose of the Study:

  • To address the challenge of reliability in data anonymization tools.
  • To investigate the impact of numerical approximations on privacy guarantees.
  • To develop and evaluate a reliable computing framework for data anonymization.

Main Methods:

  • Developed a reliable computing framework utilizing fractional and interval arithmetic.
  • Implemented and tested the framework for data anonymization applications.
  • Conducted extensive evaluations to assess reliability, execution time, and data utility.

Main Results:

  • Demonstrated the practicality of reliable data anonymization.
  • Showcased that the proposed framework improves the reliability of privacy implementations.
  • Evaluations indicated minor impacts on execution times and data utility.

Conclusions:

  • Reliable computing frameworks are essential for trustworthy data anonymization in practice.
  • Fractional and interval arithmetic offer a viable solution for enhancing the accuracy of privacy models.
  • The developed approach enables practical, reliable health data anonymization without significant performance or utility trade-offs.