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

Drug Nomenclature01:17

Drug Nomenclature

During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that the same...
Drug Regulation01:25

Drug Regulation

Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

You might also read

Related Articles

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

Sort by
Same author

Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis.

Frontiers in digital health·2026
Same author

Health inequalities in outpatient neurological conditions across a large UK urban population: a retrospective observational study using automated coding.

BMJ neurology open·2026
Same author

Development and validation of prediction models for predicting social care strengths and vulnerability in older people: Cohort study using routine data in Adult Social Care.

PloS one·2026
Same author

Towards a resource for multilingual lexicons: an MT assisted and human-in-the-loop multilingual parallel corpus with multi-word expression annotation.

Language resources and evaluation·2026
Same author

Extending BEHRT to UK Biobank: assessing transformer model performance in clinical prediction.

Frontiers in digital health·2026
Same author

A practical and nuanced framework for entity linking evaluation.

Journal of biomedical semantics·2026

Related Experiment Video

Updated: Jun 9, 2026

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

Medication information extraction with linguistic pattern matching and semantic rules.

Irena Spasic1, Farzaneh Sarafraz, John A Keane

  • 1Cardiff School of Computer Science & Informatics, Cardiff University, Cardiff, UK. i.spasic@cs.cardiff.ac.uk

Journal of the American Medical Informatics Association : JAMIA
|September 8, 2010
PubMed
Summary
This summary is machine-generated.

This study developed a rule-based system for extracting medication details from clinical notes, achieving an 81% F-measure. The system effectively identifies medication name, dosage, and frequency, aiding in clinical data analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 9, 2026

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

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:

  • Natural Language Processing (NLP) in Healthcare
  • Clinical Informatics
  • Biomedical Text Mining

Background:

  • Automated extraction of medication information from clinical text is crucial for patient care and research.
  • Manual review of medical reports is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and evaluate a system for automatically extracting medication details from clinical notes.
  • To identify medication name, dosage, mode/route, frequency, duration, and reason from patient reports.

Main Methods:

  • A rule-based methodology utilizing morphological, lexical, syntactic, and semantic features.
  • Information acquired from training data, UMLS, and web resources.
  • Pattern matching combined with context-sensitive heuristic rules.

Main Results:

  • Achieved a micro-averaged F-measure of 81% (86% precision, 77% recall), ranking third in the 2009 i2b2 Challenge.
  • Demonstrated strong performance in extracting medication duration (53% F-measure) and reason (46% F-measure).
  • System performance was comparable to the second-ranked system and not significantly different.

Conclusions:

  • The system's performance (81% F-measure) aligns with human annotator agreement (82%).
  • This NLP system can significantly streamline the extraction of medication information from medical records.
  • Provides a foundation for efficient manual review of clinical data.