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Related Concept Videos

SBAR I: Understanding the Concept01:29

SBAR I: Understanding the Concept

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Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
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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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Basic Concept01:28

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Engineering mechanics is a branch of engineering that studies motion and the forces acting on objects. It is a fundamental subject and forms the basis of many other engineering disciplines. Length, time, mass, and force are some basic concepts in engineering mechanics.
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Concepts and Prototypes01:24

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Clinical concept and relation extraction using prompt-based machine reading comprehension.

Cheng Peng1, Xi Yang1,2, Zehao Yu1

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Journal of the American Medical Informatics Association : JAMIA
|June 15, 2023
PubMed
Summary
This summary is machine-generated.

A novel unified prompt-based machine reading comprehension (MRC) system enhances clinical concept and relation extraction. This natural language processing approach shows superior performance and generalizability across institutions.

Keywords:
clinical concept extractionmachine reading comprehensionnatural language processingrelation extractiontransformer model

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Machine Learning

Background:

  • Clinical concept and relation extraction are crucial for biomedical research and clinical decision support.
  • Existing methods often require separate models for concept and relation extraction, limiting efficiency and generalizability.
  • Developing unified systems with strong cross-institutional performance remains a challenge.

Purpose of the Study:

  • To develop a unified prompt-based machine reading comprehension (MRC) architecture for clinical concept and relation extraction.
  • To evaluate the generalizability and transfer learning capabilities of the proposed MRC models in cross-institutional settings.
  • To compare the performance of MRC models against existing deep learning approaches for clinical NLP tasks.

Main Methods:

  • Formulated clinical concept and relation extraction using a unified prompt-based MRC architecture.
  • Explored state-of-the-art transformer models, including GatorTron-MRC and BERT-MIMIC-MRC.
  • Evaluated models on benchmark datasets from the National NLP Clinical Challenges (n2c2) 2018 and 2022, including cross-institution transfer learning.
  • Conducted error analyses and examined the impact of different prompting strategies.

Main Results:

  • The proposed MRC models achieved state-of-the-art performance on clinical concept and relation extraction tasks.
  • GatorTron-MRC demonstrated superior F1-scores for concept extraction, outperforming previous models by 1%-3%.
  • GatorTron-MRC and BERT-MIMIC-MRC achieved top F1-scores for relation extraction, outperforming prior models by 0.9%-11%.
  • MRC models showed significant improvements in cross-institution evaluation, with GatorTron-MRC outperforming traditional GatorTron by up to 16%.

Conclusions:

  • The unified prompt-based MRC architecture offers a powerful and generalizable approach for clinical concept and relation extraction.
  • The proposed models exhibit enhanced capabilities in handling complex annotations and demonstrate strong portability for cross-institute applications.
  • The developed clinical MRC package is publicly available, facilitating further research and application in clinical NLP.