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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

432
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:
432
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

197
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,...
197
Classification of Illness01:17

Classification of Illness

7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

869
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
869

You might also read

Related Articles

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

Sort by
Same author

Optimizing the Use of Proviral DNA HIV Drug Resistance Testing: Clinical Applications and Cautions.

The Journal of infectious diseases·2026
Same author

Optimizing HIV-1 Genotypic Resistance Testing for Low- and Middle-Income Countries: High-Impact HIV-1 Mutations Across WHO-Defined Scenarios.

Viruses·2026
Same author

Ultrafiltration and Sodium Removal in Steady Concentration Peritoneal Dialysis: A Prospective, Multicenter, Randomized, Crossover Study.

Journal of the American Society of Nephrology : JASN·2026
Same author

Letter to the editor: When do rare events become expected in HIV drug resistance?

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026
Same author

Longitudinal challenges faced by perinatally-infected young people with HIV in Kenya during the COVID-19 pandemic.

AIDS (London, England)·2026
Same author

A pilot randomized controlled trial to explore the feasibility of a peer-delivered single-session brief intervention for youth with moderate risk substance use.

PloS one·2026

Related Experiment Video

Updated: Jul 27, 2025

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

Comparison of machine learning methods for predicting viral failure: a case study using electronic health record

Allan Kimaina1,2,3, Jonathan Dick4,3, Allison DeLong2

  • 1Moi University, Eldoret, Kenya.

Statistical Communications in Infectious Diseases
|June 8, 2023
PubMed
Summary

Machine learning models can predict human immunodeficiency virus (HIV) viral failure before scheduled measurements. This allows for earlier interventions to improve patient outcomes in resource-limited settings.

Keywords:
HIV/AIDSclinical prediction rulesmachine learningviral failure

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
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

7.6K

Related Experiment Videos

Last Updated: Jul 27, 2025

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
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
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

7.6K

Area of Science:

  • Machine Learning
  • Public Health
  • Infectious Diseases

Background:

  • Human immunodeficiency virus (HIV) viral failure occurs when antiretroviral therapy (ART) fails to suppress viral load below 1,000 copies/mL.
  • WHO recommends viral load monitoring at 6 months and annually for HIV patients, with deviations for suspected viral failure.
  • Timely detection of viral failure is crucial for initiating essential interventions, and clinical prediction models offer potential for early detection.

Purpose of the Study:

  • To compare the predictive accuracy of statistical machine learning methods for forecasting HIV viral failure.
  • To utilize electronic health record (EHR) data from a large HIV care program in Kenya.
  • To predict viral failure at the first and second measurements post-ART initiation.

Main Methods:

  • Trained and cross-validated 10 statistical machine learning models (parametric, non-parametric, ensemble, Bayesian) on over 10,000 patient records.
  • Utilized 50 clinician-selected variables from clinical records as input.
  • Calculated predictive accuracy using 10-fold cross-validation, measuring sensitivity, specificity, and AUC.

Main Results:

  • Viral failure rate was approximately 20% at both first and second measurements.
  • Ensemble methods generally outperformed others, with specificity >90% and sensitivity 50-60% for the first measurement.
  • Predictive accuracy improved for the second measurement, with sensitivities >80%; Super Learner, gradient boosting, and BART were top performers.
  • Top methods achieved positive predictive values of 75-85% and negative predictive values >95% for a 20% failure rate.

Conclusions:

  • Machine learning techniques show promise in identifying patients at risk for HIV viral failure before scheduled monitoring.
  • Prognostic virologic assessment can guide earlier, targeted interventions like resistance monitoring, adherence counseling, or therapy switching.
  • External validation is recommended to confirm these findings and support clinical implementation.