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Related Concept Videos

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Models, Theories, and Laws01:16

Models, Theories, and Laws

Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...

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Related Experiment Videos

Common data model for natural language processing based on two existing standard information models: CDA+GrAF.

Stéphane M Meystre1, Sanghoon Lee, Chai Young Jung

  • 1Department of Biomedical Informatics, University of Utah, School of Medicine, Salt Lake City, UT 84112, USA. stephane.meystre@hsc.utah.edu

Journal of Biomedical Informatics
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

A new CDA+GrAF data model enhances collaboration in clinical Natural Language Processing (NLP). This model integrates existing standards for shared annotation and resource use in NLP research.

Related Experiment Videos

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing

Background:

  • The Natural Language Processing (NLP) community requires enhanced collaboration and resource sharing for clinical text analysis.
  • Existing standards lack a unified data model for annotated clinical information.

Purpose of the Study:

  • To develop a common data model, CDA+GrAF, integrating HL7 Clinical Document Architecture (CDA) and ISO Graph Annotation Format (GrAF).
  • To facilitate standardized annotation and resource sharing in clinical NLP research and development.

Main Methods:

  • Combined HL7 CDA and ISO GrAF into a standoff annotation XML document (CDA+GrAF).
  • Utilized clinical document sections and the 2010 i2b2/VA NLP Challenge for use cases.
  • Developed an automated translation tool from i2b2/VA format to GrAF.

Main Results:

  • Successfully created and validated CDA+GrAF standoff annotation documents using two use cases.
  • Generated 50 validated annotation documents automatically using the developed translation tool.
  • Adapted HL7 CDA's XSL stylesheet for web browser viewing of annotation documents.

Conclusions:

  • The CDA+GrAF model simplifies comparing NLP tools, combining outputs, and transforming annotations.
  • This common data model promotes interoperability and "plug-and-play" capabilities for NLP applications in the clinical domain.