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Related Concept Videos

Types of Reports I: Hands-off Report01:25

Types of Reports I: Hands-off Report

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A hand-off report, also known as a change-of-shift report, is a crucial nursing process that ensures the smooth transition of patient care responsibilities between nursing staff.
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Purpose and Process:
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Types of Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

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Telephone and Verbal Reports in healthcare settings are two communication methods for conveying therapeutic instructions from healthcare providers to nurses or other healthcare staff.
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Video

Updated: Aug 3, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models.

Max Tigo Rietberg1, Van Bach Nguyen2, Jeroen Geerdink3

  • 1Faculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands.

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PubMed
Summary

A Dutch language model, BERTje, accurately identified reasons for Multiple Sclerosis (MS) MRI scans, outperforming specialized models. This shows general language models can be reliable for medical report analysis.

Keywords:
BERThealth informaticsnatural language processingtext classification

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Understanding the diagnostic purpose of medical reports aids in patient flow analysis.
  • Extracting reasons for MRI scans in Multiple Sclerosis (MS) patients is crucial.

Purpose of the Study:

  • To extract the diagnostic goal (Diagnosis, Progression, or Monitoring) of MRI scans for MS patients from free-form reports.
  • To evaluate domain-specific and general state-of-the-art language models for this task.
  • To assess model alignment with domain expertise using eXplainable Artificial Intelligence (XAI).

Main Methods:

  • Investigated performance of domain-dependent and general language models.
  • Utilized eXplainable Artificial Intelligence (XAI) techniques for model insight and trustworthiness verification.
  • Compared verified XAI explanations with domain expert explanations for reliability assessment.

Main Results:

  • BERTje, a general Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperformed domain-specific models (RobBERT, MedRoBERTa.nl) in accuracy and reliability.
  • Demonstrated that domain-specific models are not always superior for this task.
  • BERTje showed promising results in a prospective study for practical application.

Conclusions:

  • General language models like BERTje can be highly effective for extracting diagnostic information from medical reports.
  • XAI techniques are valuable for verifying model trustworthiness and reliability in clinical settings.
  • The findings suggest potential for AI-driven analysis of medical reports to improve patient care and workflow.