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

Integrated Healthcare System01:20

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains for...
Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

You might also read

Related Articles

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

Sort by
Same author

AI-Based Pathology classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Precision medicine's inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and deep learning.

The Lancet. Digital health·2026
Same author

Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact.

Annals of global health·2026
Same author

Reply to Z Yu and F Qin.

The American journal of clinical nutrition·2026
Same author

Artificial Intelligence-informed Architectural Insights of 3-dimensional Glandular Networks Identify Patients With Prostate Cancer at a Higher Risk of Biochemical Recurrence.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

An integrated clinical-histopathologic prediction model for cardiac allograft rejection: Translating machine learning into clinical risk frameworks.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026
Same journal

Emerging blood-based diagnostic strategies for tuberculosis.

Clinical chemistry and laboratory medicine·2026
Same journal

Homocitrulline and 3-year mortality in older hospitalised adults: an exploratory study.

Clinical chemistry and laboratory medicine·2026
Same journal

Impact of age partitioning on classification discordance in pediatric ferritin reference intervals.

Clinical chemistry and laboratory medicine·2026
Same journal

National implementation of LOINC: translation methodology and experience from the Polish laboratory terminology standardization project.

Clinical chemistry and laboratory medicine·2026
Same journal

Harmonization of measurement units: mission impossible or ethical imperative?

Clinical chemistry and laboratory medicine·2026
Same journal

Analysis of albumin-to-creatinine ratio on automated analysers using a microsampling card (Capitainer DIP70 card) for the collection of urine.

Clinical chemistry and laboratory medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics
10:42

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Published on: June 17, 2022

Integrated diagnostics: a conceptual framework with examples.

Anant Madabhushi1, Scott Doyle, George Lee

  • 1Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA.

Clinical Chemistry and Laboratory Medicine
|May 25, 2010
PubMed
Summary
This summary is machine-generated.

Manifold learning techniques are being applied to analyze complex digital pathology images for disease diagnosis, prognosis, and therapy selection. These advanced machine learning methods aid in predicting patient outcomes, particularly when combining image data with proteomic signatures.

Related Experiment Videos

Last Updated: Jun 12, 2026

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics
10:42

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Published on: June 17, 2022

Area of Science:

  • Computational pathology
  • Machine learning in medicine
  • Digital histopathology image analysis

Background:

  • Digital pathology enables computerized analysis of histopathology images for diagnostic, prognostic, and theranostic predictions.
  • High-resolution digitized histopathology datasets are information-rich and pose challenges for traditional computer vision algorithms.
  • Manifold learning and non-linear dimensionality reduction are powerful machine learning tools for pattern recognition.

Purpose of the Study:

  • To review the application of manifold learning methods in computer-aided diagnosis, prognosis, and theragnosis of digitized histopathology.
  • To discuss advancements in using manifold learning for multi-modal data fusion and classification.
  • To highlight the development of meta-classifiers integrating histological images and proteomic signatures for prostate cancer outcome prediction.

Main Methods:

  • Application of manifold learning and non-linear dimensionality reduction techniques to analyze complex histopathology datasets.
  • Development of algorithms to handle information-rich, dense image data.
  • Fusion of multi-modal data, including histological images and proteomic signatures, for enhanced classification.

Main Results:

  • Manifold learning methods show promise in addressing the challenges of analyzing complex digitized histopathology data.
  • These techniques are being adapted from traditional computer vision problems to medical image analysis.
  • Successful integration of histological and proteomic data using meta-classifiers for improved prostate cancer outcome prediction.

Conclusions:

  • Manifold learning offers a powerful approach for extracting meaningful patterns from high-resolution digitized histopathology.
  • The fusion of imaging and molecular data holds significant potential for advancing computer-aided diagnosis and prognosis.
  • Future research directions include further development and validation of these machine learning techniques in clinical settings.