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

Review and Preview01:13

Review and Preview

9.5K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
9.5K
Biostatistics: Overview01:20

Biostatistics: Overview

1.2K
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
1.2K
Nursing Clinical Information System01:27

Nursing Clinical Information System

1.3K
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
1.3K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

29.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
29.2K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.6K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.6K
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

3.4K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Towards symbolic regression for interpretable clinical decision scores.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Session Introduction: AI and Machine Learning in Clinical Medicine Bridging or Separating Model Intelligence and Human Expertise.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

Violence Exposure, Mental Health, Cognitive Functioning, and Disabilities in Incarcerated Youth.

Research square·2026
Same author

Effect of Erenumab on Patient-Reported Outcomes in Episodic Migraine in Asia, the Middle East, and Latin America: Results From the EMPOwER Study.

Neurology. Clinical practice·2026
Same author

Expanding the genetic landscape of endometriosis: Integrative -omics analyses implicate key genes and pathways in a multi-ancestry study of over one million women.

Research square·2025

Related Experiment Video

Updated: Apr 30, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K

Clinical Knowledge Representation in Data Science.

Tram Anh Nguyen1,2, Wendy Su1,2, Ananya Rajagopalan1,2

  • 11Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Annual Review of Biomedical Data Science
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

Clinical knowledge representation (KR) transforms fragmented health data into structured formats for research. This approach enables data harmonization, interoperability, and advanced AI models for biomedical discovery and improved patient care.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.6K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.8K

Related Experiment Videos

Last Updated: Apr 30, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.6K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.8K

Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Clinical Knowledge Representation

Background:

  • Observational healthcare data holds vast potential for biomedical discovery but is often fragmented and unstructured.
  • Current clinical data formats hinder secondary use due to being generated primarily for patient care.
  • Bridging the gap requires standardized, computable formats that preserve data meaning and context.

Purpose of the Study:

  • To review clinical knowledge representation (KR) across the entire clinical data life cycle.
  • To highlight foundational components enabling data harmonization and interoperability.
  • To discuss how structured representations support AI model development for healthcare.

Main Methods:

  • Review of clinical knowledge representation strategies.
  • Examination of data generation, transformation, and secondary use.
  • Analysis of foundational components like terminologies, ontologies, and data models.

Main Results:

  • Standardized terminologies, ontologies, and common data models are crucial for data harmonization and interoperability.
  • Structured KR supports multimodal data integration.
  • Semantics-first KR advances the development of accurate and interpretable AI models.

Conclusions:

  • A semantics-first approach to KR is essential for unlocking the potential of clinical data.
  • Transforming fragmented data into reusable knowledge drives data-driven discovery.
  • Effective KR ultimately improves patient care through advanced analytics and AI.