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Related Concept Videos

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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...
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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The self-concept encompasses individuals' beliefs about themselves, structured through cognitive frameworks known as self-schemas. These schemas function as mental representations of specific traits or behaviors, influencing how self-relevant information is perceived, processed, and remembered. For example, individuals who are schematic for body weight are more likely to interpret routine experiences—such as dining out or shopping—through the lens of that trait. Conversely, those...
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Related Experiment Video

Updated: Oct 16, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation.

Asim Abbas1, Muhammad Afzal2, Jamil Hussain3

  • 1Department of Computer Science and Engineering, Global Campus, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Korea.

International Journal of Environmental Research and Public Health
|October 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new rule-based system for accurately extracting clinical concepts like problems and treatments from medical texts. The system improves upon existing methods, aiding data-driven healthcare applications.

Keywords:
clinical concept extractiondata annotationlexical semanticsmedical concept classificationrule-based systems

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Extracting clinical concepts from unstructured narrative documents is crucial for data-driven healthcare applications.
  • Existing machine/deep learning methods require labor-intensive annotated datasets.
  • Current rule-based systems often fail to capture complex linguistic patterns.

Purpose of the Study:

  • To introduce a comprehensive, rule-based system for accurate and transparent clinical concept extraction.
  • To develop a pipelined approach for recognizing problem, treatment, and test concepts.
  • To improve the annotation process for clinical data to enhance supervised learning applications.

Main Methods:

  • A pipelined, rule-based system was developed to extract clinical concepts.
  • The system was evaluated on a dataset from the I2b2 challenge 2010.
  • Performance was compared against Quick UMLS, BIO-CRF, and the Rules (i2b2) model.

Main Results:

  • The proposed system achieved an average F1-score of 72.94%, outperforming baseline systems.
  • It showed significant improvements over Quick UMLS (+13%), BIO CRF (+3%), and Rules (i2b2) (+30.1%).
  • Individual concept extraction performance was highest for problems (80.45%), followed by treatments (76.06%) and tests (55.3%).

Conclusions:

  • The developed rule-based system significantly enhances the accuracy and transparency of clinical concept extraction.
  • Leveraging linguistic and lexical semantic features improves performance over existing methods.
  • This approach facilitates automatic annotation, boosting the efficacy of data-driven healthcare tools.