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

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

143
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
143
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

189
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
189
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

101
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
101
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

103
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
103
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

125
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
125
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

80
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
80

You might also read

Related Articles

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

Sort by
Same author

Using Natural Language Processing to Characterize Early Steps in the Kidney Transplant Evaluation Process Documented in the National Veterans Affairs Electronic Health Record.

Clinical transplantation·2026
Same author

Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.

JACC. Advances·2025
Same author

Calcium ion nanomodulators induce pyroptosis for tumor immunotherapy.

Nanoscale·2025
Same author

Electrocatalytic Alcohol Oxidation to Aldehyde Through Direct Dehydrogenation Mechanism Using a High-Performance Pt/Co<sub>3</sub>O<sub>4</sub> Catalyst.

Angewandte Chemie (International ed. in English)·2025
Same author

Highly sensitive ultraviolet power meter based on two-dimensional van der Waals heterostructure.

Nanotechnology·2025
Same author

Highly sensitive and spectrally tunable UV photodetectors <i>via</i> interface barrier engineering in floating-gate transistors.

Nanoscale·2025

Related Experiment Video

Updated: Jul 30, 2025

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

16.0K

A deep learning approach for medication disposition and corresponding attributes extraction.

Qiwei Gan1, Mengke Hu1, Kelly S Peterson2

  • 1VA Salt Lake City Health Care System, 500, Foothill Boulevard, Salt Lake City 84148, USA; Division of Epidemiology, University of Utah, 295 Chipeta Way, Salt Lake City 84132, USA.

Journal of Biomedical Informatics
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a deep learning NLP system for extracting medication information from clinical notes. The system achieved high accuracy in identifying medications, events, and their contexts, improving clinical data analysis.

Keywords:
Clinical natural language processingConcept-attribute relation classificationMedication information extraction

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 30, 2025

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

16.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Area of Science:

  • Natural Language Processing (NLP)
  • Clinical Informatics
  • Machine Learning

Background:

  • Accurate extraction of medication information from clinical notes is crucial for patient safety and effective healthcare.
  • Existing methods often struggle with the complexity and nuances of clinical documentation.
  • The 2022 National Natural Language Processing (NLP) Clinical Challenges (n2c2) task 1 focused on medication attribute extraction.

Purpose of the Study:

  • To develop and evaluate a deep learning-based NLP system for extracting medication and associated attributes from clinical notes.
  • To address the challenges of medication named entity recognition (NER), event classification (EC), and context classification (CC).
  • To explore novel approaches, including zero-shot learning for context classification.

Main Methods:

  • A three-component system was developed: medication NER, EC, and CC.
  • Transformer models with customized architectures and input text engineering were employed.
  • The Contextualized Medication Event Dataset (CMED), comprising 500 notes from 296 patients, was utilized.
  • Zero-shot learning was investigated for the context classification component.

Main Results:

  • The system achieved high performance across all components.
  • Micro-average F1 scores were 0.973 for NER, 0.911 for EC, and 0.909 for CC.
  • The approach demonstrated the effectiveness of special tokens for disambiguating medication mentions and aggregating events.

Conclusions:

  • The implemented deep learning NLP system effectively extracts medication information and attributes from clinical notes.
  • Utilizing special tokens and aggregating multiple medication events significantly improved model performance.
  • The findings contribute to advancing NLP applications in clinical settings for better data utilization.