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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...
Introduction to Language of Pathophysiology l01:25

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...
Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...
Nursing Clinical Information System01:27

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
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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.
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

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Related Experiment Video

Updated: May 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

A Hybrid Language Framework for Ontology-Based Clinical Concept Extraction.

Behnaz Eslami1, Dmitriy Dligach1, Nazanin Azarvash2

  • 1Department of Computer Science, Loyola University Chicago, Chicago, IL 60626 USA.

Journal of Healthcare Informatics Research
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid framework for clinical concept extraction from EHRs using large language models (LLMs) and ontologies. LLaMA3-8B achieved the highest accuracy, outperforming traditional methods.

Keywords:
Concept extraction. hybrid LLM. clinical ontology

Related Experiment Videos

Last Updated: May 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

  • Natural Language Processing
  • Biomedical Informatics
  • Artificial Intelligence

Background:

  • Electronic Health Records (EHRs) contain valuable clinical information in unstructured narrative text.
  • Extracting clinical concepts accurately from EHRs is crucial for downstream applications like clinical decision support and research.
  • Existing methods often struggle with the complexity and nuances of clinical language.

Purpose of the Study:

  • To develop and evaluate a hybrid ontology-based framework for clinical concept extraction from EHR discharge summaries.
  • To compare the performance of large language models (LLMs) against traditional systems for this task.
  • To assess the effectiveness of integrating NLP, semantic similarity, and biomedical terminologies.

Main Methods:

  • A sequential NLP pipeline was developed, incorporating SparkNLP for initial processing, SentenceBERT for semantic similarity, and LLMs (LLaMA3-8B, Mistral-7B) for concept selection.
  • UMLS REST API was used for normalization to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) and SNOMED CT.
  • The framework was tested on MIMIC-III discharge summaries, with clinician-based evaluation of extracted concepts.

Main Results:

  • LLaMA3-8B achieved the highest F1 score (0.77) and the lowest false positive rate (3.04%), outperforming Mistral-7B and cTAKES.
  • LLMs demonstrated superior ability in handling clinical language complexities compared to rule-based systems.
  • Mistral-7B offered faster processing for shorter notes, while LLaMA3-8B excelled in accuracy for detailed sections.

Conclusions:

  • The proposed hybrid framework offers a flexible and context-aware approach to clinical concept extraction.
  • LLMs integrated within this framework show significant promise for improving the accuracy and efficiency of clinical information extraction from EHRs.
  • Future research should focus on full ontology mapping, assertion detection, and validation on diverse datasets.