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

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...

You might also read

Related Articles

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

Sort by
Same author

Multi-scale data improves performance of machine learning model for long COVID identification.

Communications medicine·2026
Same author

Governing real-world health data as a public utility.

Science (New York, N.Y.)·2026
Same author

LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.

Brain sciences·2025
Same author

LinkML: an open data modeling framework.

GigaScience·2025
Same author

Development of a robust corpus for automated evaluation of online health information in Chinese using the DISCERN scale.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Advancing the science of genomic learning healthcare systems.

Learning health systems·2025
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

LexValueSets: an approach for context-driven value sets extraction.

Jyotishman Pathak1, Guoqian Jiang, Sridhar O Dwarkanath

  • 1Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a novel approach for automatically extracting medical value sets using context patterns and formal terminology models. This method enhances the sharing and reuse of clinical data across healthcare systems.

Related Experiment Videos

Last Updated: Jun 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Medical Informatics
  • Health Information Technology

Background:

  • Sharing and reusing value sets across medical information systems is crucial but challenging.
  • Semi-automatic generation of value sets from terminology services is hindered by the lack of clinical context linkage.

Purpose of the Study:

  • To develop and evaluate a context-driven approach for automatic value set extraction.
  • To address the limitations in defining concept domains and invoking value set extraction.

Main Methods:

  • Developed a technique leveraging context patterns from various discourse domains.
  • Utilized two complementary methods: extensional (Subject Matter Expert terms) and intensional (semantic concept definitions).
  • Implemented a prototype using SNOMED CT within the LexGrid terminology model.

Main Results:

  • Demonstrated a feasible approach for context-driven value set extraction.
  • Showcased the integration of local terms and semantic definitions for improved accuracy.
  • Presented preliminary evaluation results of the implemented prototype.

Conclusions:

  • The proposed approach facilitates automatic value set extraction by incorporating clinical context.
  • This method improves the modeling, sharing, and reuse of value sets in medical information systems.
  • Further evaluation is warranted to fully realize the potential of context-driven value set generation.