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

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

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

Sort by
Same author

Association of HOXB13 G84E With Prostate Cancer Among 592,158 Men.

Journal of the National Comprehensive Cancer Network : JNCCN·2025
Same author

The Best of All Worlds: A Hybrid Approach to Cohort Identification with Rules, Small and Large Language Models.

Studies in health technology and informatics·2025
Same author

Enhancing and Disaggregating Native Hawaiian and Pacific Islander (NHPI) Data Using Natural Language Processing and an Expanded Race/Ethnicity Lexicon.

Studies in health technology and informatics·2025
Same author

Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.

NPJ digital medicine·2025
Same author

Drug-Gene Interactions and Clinical Outcomes After Vascular Surgery in the Million Veteran Program.

JAMA surgery·2025
Same author

Risk of Thyroid Tumors With GLP-1 Receptor Agonists: A Retrospective Cohort Study.

Diabetes care·2025
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 Videos

Building a common pipeline for rule-based document classification.

Olga V Patterson1, Thomas Ginter, Scott L DuVall

  • 1VA Salt Lake City Health Care System, Salt Lake City, UT, USA.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a five-step process for developing rule-based natural language processing (NLP) systems for clinical text classification. This method empowers small teams to efficiently extract valuable insights from medical data.

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Instance-based classification of clinical text is crucial for patient classification, document retrieval, and information extraction.
  • Traditional rule-based approaches require extensive concept identification and context analysis.
  • Limited resources and tight timelines often hinder the development of clinical NLP systems.

Purpose of the Study:

  • To propose a streamlined five-step process for creating effective rule-based NLP systems for clinical text.
  • To enable small research teams to develop robust classification models with limited resources.
  • To leverage a common UIMA AS (Web Services) based pipeline for efficient classification.

Main Methods:

  • A five-step methodology for developing rule-based NLP systems.
  • Utilizing a general-purpose, UIMA AS-based pipeline for text classification.
  • Focusing on concept identification and context analysis within clinical text.

Main Results:

  • The proposed process facilitates the development of simple yet powerful rule-based NLP systems.
  • Researchers can gain access to data within clinical text more efficiently.
  • The methodology is suitable for teams with limited human resources and compact timelines.

Conclusions:

  • The presented methodology offers a practical solution for clinical text classification.
  • It democratizes access to clinical data insights for smaller research groups.
  • The UIMA AS-based pipeline enhances the efficiency and accessibility of NLP tool development.