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

26
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...
26
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

291
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
291
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

80
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,...
80
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

584
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
584
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.2K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.2K

You might also read

Related Articles

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

Sort by
Same author

An Efficient System for Eye Movement Desensitization and Reprocessing (EMDR) Therapy: A Pilot Study.

Healthcare (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2025

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

8.2K

Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning

Gabriel-Vasilică Sasu1, Bogdan-Iulian Ciubotaru2, Nicolae Goga3

  • 1Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) effectively handle missing data in elderly frailty monitoring systems. These methods outperform others across various missing data scenarios, improving healthcare data quality.

Keywords:
MARMCARMNARdeep learningfrailty detectiongeriatric healthcarehealth monitoring systemsmachine learningmissing data imputationwearable sensors

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
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

14.4K

Related Experiment Videos

Last Updated: May 28, 2025

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

8.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
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

14.4K

Area of Science:

  • Geriatric Healthcare Technology
  • Data Science and Machine Learning
  • Biomedical Informatics

Background:

  • Missing data presents a significant challenge in geriatric healthcare, particularly for frailty monitoring systems.
  • Maintaining data quality is crucial for the performance of models detecting frailty insights in elderly individuals.

Purpose of the Study:

  • To explore advanced imputation techniques for enhancing data quality in elderly frailty monitoring systems.
  • To evaluate the performance of various imputation methods under different missing data mechanisms (MCAR, MAR, MNAR).

Main Methods:

  • Simulated missing data mechanisms (MCAR, MAR, MNAR) in a smart bracelet dataset.
  • Evaluated imputation methods: EM, matrix completion, Bayesian networks, KNN, SVM, GAINs, VAE, and GRU-D.
  • Performance metrics included normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R².

Main Results:

  • K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) consistently outperformed other methods across all missing data mechanisms.
  • KNN and SVM demonstrated superior performance in Missing at Random (MAR) and Missing Completely at Random (MCAR) conditions.
  • KNN and SVM proved robust in Missing Not at Random (MNAR) contexts, outperforming GRU-D and VAE.

Conclusions:

  • The choice of imputation method should be based on the specific characteristics of missing data mechanisms.
  • KNN and SVM offer versatility and reliability for handling missing data in healthcare applications.
  • Hybrid approaches, like those in the cINnAMON project, are advocated for supporting at-risk elderly individuals.