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

Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

250
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
250
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

6.3K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
6.3K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

544
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
544
Pedigree Analysis01:35

Pedigree Analysis

90.4K
Overview
90.4K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.6K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.6K
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.

The annals of applied statistics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same author

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Journal of machine learning research : JMLR·2026
Same author

Data fusion methods for the heterogeneity of treatment effect and confounding function.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability·2026
Same author

Leveraging precision medicine analytics to optimize inflammation reduction and enhance physical function in older adults.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Related Experiment Video

Updated: Mar 10, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K

Estimating personalized diagnostic rules depending on individualized characteristics.

Ying Liu1, Yuanjia Wang2, Chaorui Huang3

  • 1Division of Biostatistics, Institute for Health and Society, Medical College of Wisconsin, Milwaukee, 53226, WI, U.S.A.

Statistics in Medicine
|December 6, 2016
PubMed
Summary
This summary is machine-generated.

Personalized diagnostic rules using machine learning can optimize medical screening. These methods improve diagnostic accuracy by assigning the most effective imaging modality to individual patients, outperforming general strategies.

Keywords:
Parkinson's diseasepersonalized screeningweighted support vector machine

More Related Videos

Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection
10:02

Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection

Published on: December 19, 2025

654
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

16.4K

Related Experiment Videos

Last Updated: Mar 10, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K
Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection
10:02

Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection

Published on: December 19, 2025

654
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

16.4K

Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Personalized medicine

Background:

  • Growing need for personalized disease screening based on individual patient risk.
  • Limitations of 'one-size-fits-all' screening guidelines in medical diagnostics.
  • Variability in diagnostic performance of imaging modalities across different subjects.

Purpose of the Study:

  • To propose novel machine learning methods for estimating personalized diagnostic rules.
  • To optimize medical screening and diagnosis by maximizing sensitivity and specificity across patient subgroups.
  • To develop strategies for both paired and unpaired imaging study designs.

Main Methods:

  • Development of machine learning algorithms for personalized diagnostic rule estimation.
  • Application of methods to paired designs (subjects receiving multiple modalities).
  • Adaptation of methods for unpaired designs (subjects receiving single modalities).

Main Results:

  • Theoretical properties (consistency, risk bounds) of personalized diagnostic rules were studied.
  • Simulation studies demonstrated the performance of the proposed machine learning methods.
  • Analysis of brain imaging data for Parkinson's disease showed improved diagnostic accuracy with personalized assignment.

Conclusions:

  • Personalized modality assignment can enhance diagnostic performance compared to uniform strategies.
  • Proposed machine learning methods offer a data-driven approach to optimize medical screening.
  • The findings have implications for improving diagnostic accuracy in various medical fields, including neurology.