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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

151
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
151
Language and Cognition01:27

Language and Cognition

529
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
529

You might also read

Related Articles

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

Sort by
Same author

Pentacyclic triterpenoids from <i>Ziziphus jujuba</i> Lamk. fruits as dual inhibitors of PTP1B and α-glucosidase: <i>in vitro</i> and <i>in silico</i> evaluations.

RSC advances·2026
Same author

FDA-Approved Drugs Containing D-Amino Acids: A Historical and Developmental Perspective.

Drug development research·2026
Same author

Exploring the diverse binding ability of SARS-CoV-2 variant RBDs to different antibody classes: a computational study.

RSC advances·2026
Same author

Relationship between cortical electrical responsiveness and changes in regional cerebral oxygenation (rSO<sub>2</sub>) and return of spontaneous circulation in prolonged cardiac arrest: a multi-center observational study.

Resuscitation·2026
Same author

Abrogation of Oncogenic RAS Signaling by a RAS(ON) Inhibitor Doublet Primes Immune-Refractory KRASG12C-Mutant NSCLC for Immune Checkpoint Blockade.

Cancer discovery·2026
Same author

Pyridinium alkaloids - a unique class of naturally occurring salt-form secondary metabolites: a comprehensive review of 68 years (1958-mid-2025).

Phytochemistry·2026
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Nov 4, 2025

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

809

Evaluating Medical Lexical Simplification: Rule-Based vs. BERT.

Linh Tran1, Erick Velazquez2, Robert-Jan Sips2

  • 1Vrije Universiteit, Amsterdam, The Netherlands.

Studies in Health Technology and Informatics
|May 27, 2021
PubMed
Summary
This summary is machine-generated.

Lexical simplification improves medical communication by replacing complex terms. An unsupervised BERT model excelled in simplicity, while a rule-based approach better preserved meaning.

Keywords:
Health VocabularyLexical SimplificationMachine Learning

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.4K
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

823

Related Experiment Videos

Last Updated: Nov 4, 2025

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

809
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.4K
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

823

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Communication

Background:

  • Lexical simplification (LS) bridges the communication gap between medical professionals and the public.
  • Replacing technical medical jargon with accessible language is crucial for patient understanding and engagement.

Purpose of the Study:

  • To develop and evaluate two distinct approaches for lexical simplification in a medical context.
  • To compare a rule-based method with an unsupervised machine learning model for generating simplified medical terms.

Main Methods:

  • A rule-based lexical simplification system utilizing a consumer health vocabulary.
  • An unsupervised approach employing BERT (Bidirectional Encoder Representations from Transformers) for generating simplified word candidates.
  • Human evaluation to assess the quality of simplified terms based on simplicity, grammaticality, and meaning preservation.

Main Results:

  • The unsupervised BERT-based model demonstrated superior performance in terms of simplicity and grammatical correctness.
  • The rule-based approach showed an advantage in preserving the original meaning of medical terms.
  • Human evaluators found the unsupervised method more effective for general simplification.

Conclusions:

  • Both rule-based and unsupervised methods offer viable strategies for lexical simplification in healthcare.
  • The choice of method depends on the specific priorities: simplicity and grammaticality versus meaning fidelity.
  • Further research can explore hybrid approaches to leverage the strengths of both methods for enhanced medical communication.