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

Improving Translational Accuracy02:07

Improving Translational Accuracy

15.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.8K
3.8K
Scale-Up Processes01:14

Scale-Up Processes

69
The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
69
Scaling01:26

Scaling

658
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
658

You might also read

Related Articles

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

Sort by
Same author

Can high private cough syrup sales act as a proxy for missed TB notifications in TB surveillance?

Public health action·2025
Same author

Ultrasound-guided pulsed radiofrequency of zygomaticotemporal nerve for refractory temporal headaches.

Anaesthesia reports·2025
Same author

Heat shock protein D1 is up-regulated in various types of canine mammary tumors.

Iranian journal of veterinary research·2025
Same author

Outcome of hemiarthroplasty to total hip arthroplasty conversion: a systematic review.

Musculoskeletal surgery·2025
Same author

Molecular Characterization of δβ Thalassemia/Hereditary Persistence of Fetal Hemoglobin and Its Correlation With Clinical and Hematological Profile; a Single Center Study in North India.

International journal of laboratory hematology·2024
Same author

Clinical course, management and outcomes of COVID-19 in HIV-infected renal transplant recipients: A case series.

South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde·2024

Related Experiment Video

Updated: Mar 30, 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

1.3K

Scaling-up NLP Pipelines to Process Large Corpora of Clinical Notes.

G Divita1, M Carter, A Redd

  • 1Guy Divita, University of Utah School of Medicine, Division of Epidemiology, 295 Chipeta Way, Salt Lake City, UT 84132, USA,

Methods of Information in Medicine
|November 5, 2015
PubMed
Summary

This study scaled up natural language processing (NLP) for clinical notes, significantly improving processing speed for big data analytics in healthcare. The NLP pipeline achieved a 12-fold performance increase, enabling efficient analysis of large patient record corpora.

Keywords:
Natural language processingbig datascale-up

More Related Videos

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

16.6K

Related Experiment Videos

Last Updated: Mar 30, 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

1.3K
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

16.6K

Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Big Data Analytics

Background:

  • Focuses on "Big Data and Analytics in Healthcare" theme.
  • Addresses the challenge of processing large volumes of clinical notes.

Purpose of the Study:

  • Describe scale-up efforts for a natural language processing (NLP) pipeline at the VA Salt Lake City Health Care System.
  • Detail a project detecting indwelling urinary catheters and associated infections in hospitalized patients.

Main Methods:

  • Developed an NLP algorithm (v3NLP) to identify indwelling urinary catheters.
  • Employed multi-threading, intra-annotator threading, and remote annotator services for pipeline scale-up.
  • Estimated processing times for corpora of 550,000 and 6 million clinical notes.

Main Results:

  • Reduced average record processing time from 206ms to 17ms.
  • Achieved a 12-fold performance increase on a 550,000-note corpus.
  • Demonstrated efficient processing of large clinical note datasets.

Conclusions:

  • Scale-up efforts provide a straightforward evolution for NLP processing of big data.
  • Techniques are generalizable and applicable to other computationally complex NLP pipelines.
  • Aids in processing and analyzing big data in healthcare settings.