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 Experiment Video

Updated: May 30, 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

Using Amazon's Mechanical Turk for Annotating Medical Named Entities.

Meliha Yetisgen-Yildiz1, Imre Solti, Fei Xia

  • 1Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|July 26, 2011
PubMed
Summary

Amazon Mechanical Turk (AMT) is used for Natural Language Processing (NLP) research. This study explores AMT

Related Concept Videos

Anatomical Terminology01:20

Anatomical Terminology

Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...

You might also read

Related Articles

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

Sort by
Same author

Multimodal habitat radiomics based on automated breast volume scanning and conventional ultrasound for risk stratification of biopsy-confirmed BI-RADS 4A breast lesions.

Frontiers in oncology·2026
Same author

Oxymatrine suppresses neuroinflammation via promoting microglial M2 polarization through FTO-dependent m<sup>6</sup>A demethylation of PGC-1α mRNA.

Scientific reports·2026
Same author

Downregulation of fibroblast growth factor 7 impairs endometrial receptivity and decidualisation via extracellular signal-regulated kinase and C-Jun N-Terminal kinase pathways, contributing to recurrent spontaneous abortion.

Journal of molecular histology·2026
Same author

High-Precision MEMS Resonant Pressure Sensor for Real-Time Barometric Monitoring.

Micromachines·2026
Same author

Comparison of food allergic immune responses in sensitized female and male Balb/c mouse induced by Lateolabrax japonicus tropomyosin.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same author

A unified analysis of global riverine eDNA reveals common associations of fish biodiversity with drainage characteristics.

Nature ecology & evolution·2026

Area of Science:

  • Biomedical Natural Language Processing (NLP)
  • Crowdsourcing in Research

Background:

  • Amazon Mechanical Turk (AMT) is increasingly utilized in Natural Language Processing (NLP) research.
  • The need for annotated biomedical text in clinical trial descriptions is growing.

Purpose of the Study:

  • To evaluate the effectiveness of Amazon Mechanical Turk (AMT) for annotating biomedical text.
  • To identify key entity types in clinical trial descriptions: medical conditions, medications, and laboratory tests.
  • To analyze the quality and characteristics of annotations provided by AMT workers.

Main Methods:

  • Utilizing Amazon Mechanical Turk (AMT) for data annotation tasks.
  • Extracting text from clinical trial descriptions.
  • Annotating text for three specific entity types: medical condition, medication, and laboratory test.

Related Experiment Videos

Last Updated: May 30, 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

  • Observing and analyzing worker performance and annotation consistency.

    • Main Results:

    • AMT can be effectively employed for annotating biomedical text from clinical trials.
    • Identified specific challenges and patterns in crowdsourced annotations for medical entities.
    • Demonstrated the feasibility of using crowdsourcing for large-scale biomedical text annotation.

    Conclusions:

    • Amazon Mechanical Turk (AMT) presents a viable and scalable solution for biomedical text annotation in NLP research.
    • Understanding worker annotation patterns is crucial for optimizing crowdsourcing in this domain.
    • This approach facilitates the creation of valuable datasets for advancing clinical NLP applications.