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Related Concept Videos

Polymer Classification: Architecture01:14

Polymer Classification: Architecture

Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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,
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 Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.

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Related Experiment Video

Updated: Jun 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

The Yale cTAKES extensions for document classification: architecture and application.

Vijay Garla1, Vincent Lo Re, Zachariah Dorey-Stein

  • 1Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, USA. vijay.garla@yale.edu

Journal of the American Medical Informatics Association : JAMIA
|May 31, 2011
PubMed
Summary
This summary is machine-generated.

Yale cTAKES Extensions (YTEX) simplify clinical document classification. This open-source system achieves high accuracy in identifying hepatic decompensation findings in radiology reports.

Related Experiment Videos

Last Updated: Jun 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Decision Support

Background:

  • Open-source clinical natural-language-processing (NLP) systems facilitate document classification.
  • Clinical NLP systems annotate clinical text but face challenges in feature extraction and representation.
  • Developing effective clinical document classifiers requires robust feature engineering.

Purpose of the Study:

  • To develop and evaluate extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES).
  • To simplify feature extraction and representation for clinical document classification.
  • To create a system for classifying radiology reports indicative of hepatic decompensation.

Main Methods:

  • Developed Yale cTAKES Extensions (YTEX) to enhance cTAKES functionality.
  • YTEX simplifies feature extraction and experimentation with various feature representations.
  • Evaluated YTEX on classifying radiology reports for hepatic decompensation indicators using rule-based and machine-learning approaches.

Main Results:

  • Achieved an F(1)-Score of 96% for retrieving abdominal radiology reports.
  • Demonstrated high accuracy in identifying specific findings: 79% for liver masses, 91% for ascites, and 95% for varices.
  • YTEX effectively supports the classification of clinical documents.

Conclusions:

  • The Yale cTAKES Extensions (YTEX) provide a valuable tool for clinical document classification.
  • The system demonstrates high performance in identifying key indicators of hepatic decompensation in radiology reports.
  • YTEX is released as open-source software to promote further development and application in clinical NLP.