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

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

You might also read

Related Articles

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

Sort by
Same author

The Role of Artificial Intelligence in Enhancing Quality of Care in Nursing Homes: A Rapid Review.

Healthcare (Basel, Switzerland)·2026
Same author

Contract Labor Reliance and Subsequent Patient Experience Performance: Evidence from U.S. Short-Term Acute Care Hospitals.

Healthcare (Basel, Switzerland)·2026
Same author

Cybersecurity in Healthcare: A Systematic Review and Narrative Analysis.

Applied clinical informatics·2026
Same author

The predictive factors of US hospital bankruptcy - a multi-model comparison.

Health care management science·2026
Same author

Project RUSH: Implementing and evaluating a community-based teen pregnancy prevention program among Hispanic youth in rural South Texas.

Public health in practice (Oxford, England)·2026
Same author

The Utilization, Application, and Impact of Institutional Special Needs Plans (I-SNPs) in Nursing Facilities: A Rapid Review.

Healthcare (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 13, 2026

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

A diagnostic methodology for hazy data with "borderline" cases.

Ramalingam Shanmugam1

  • 1School of Health Administration, Texas State University, San Marcos, TX 78666, USA. rs25@txstate.edu

Journal of Medical Systems
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

Diagnosing illnesses with borderline cases requires a new approach. This study develops a novel methodology to accurately analyze diagnostic test data for conditions like hypertension and dementia, improving medical assessments.

Related Experiment Videos

Last Updated: Jun 13, 2026

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

Area of Science:

  • Medical diagnostics
  • Biostatistics
  • Clinical research

Background:

  • Many illnesses, including hypertension and dementia, present with borderline cases.
  • These borderline cases are difficult to classify as healthy or diseased.
  • Current diagnostic methods are inadequate for handling such ambiguous data.

Purpose of the Study:

  • To develop and present a new methodology for analyzing diagnostic test data with borderline cases.
  • To address the limitations of existing diagnostic test methodologies.
  • To improve the interpretation of medical parameters in the presence of ambiguous health states.

Main Methods:

  • Development of a novel analytical methodology.
  • Application of the methodology to datasets containing borderline cases.
  • Calculation and interpretation of sensitivity, specificity, and disease prevalence.

Main Results:

  • The new methodology provides a framework for analyzing diagnostic data with borderline cases.
  • Accurate calculation and interpretation of key medical parameters (sensitivity, specificity, prevalence) were demonstrated.
  • The approach was successfully illustrated using data from borderline dementia and hypertension cases.

Conclusions:

  • A new methodology is essential for accurate diagnosis in cases with borderline health states.
  • This approach enhances the interpretation of diagnostic test results for conditions like dementia and hypertension.
  • The developed methodology offers a more robust tool for medical professionals dealing with ambiguous patient data.