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Related Concept Videos

Nursing Clinical Information System01:27

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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.
Critical attributes of NCIS include:
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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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Diagnosing Pulmonary EmbolismDiagnosing pulmonary embolism (PE) involves clinical assessment and advanced imaging tests. The preferred diagnostic tool is the spiral (helical) CT scan or CT angiography (CTA), which uses intravenous contrast media to visualize the pulmonary vasculature and identify emboli.A ventilation-perfusion (V/Q) scan is an alternative for patients unable to receive contrast media. This scan includes both perfusion and ventilation scanning. Perfusion scanning involves...
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Related Experiment Video

Updated: Nov 9, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
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Embedding, aligning and reconstructing clinical notes to explore sepsis.

Xudong Zhu1, Joseph M Plasek2, Chunlei Tang2,3

  • 1Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China.

BMC Research Notes
|April 15, 2021
PubMed
Summary

New tools for exploratory analysis improve how machine learning models understand clinical notes. This enhances data representation and model performance for data-driven medicine, particularly in sepsis research.

Keywords:
Data driven medicineExploratory analysisRepresentation learningSepsis

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Clinical notes are underrepresented in data insights, limiting medically relevant applications.
  • Developing tools for exploratory analysis of clinical narratives is crucial for enhancing data diversity.

Purpose of the Study:

  • To research and develop exploratory analysis tools for clinical notes.
  • To investigate the impact of exploratory analysis on representation learning for clinical narratives.

Main Methods:

  • Developed self-created tools for exploring sepsis data.
  • Utilized the MIMIC-III Clinical Database and institutional research patient data.
  • Focused on representation learning, temporal expression analysis, and data clustering.

Main Results:

  • Global embeddings aid in learning local representations of clinical notes.
  • Time alignment facilitates model training by pooling clinical notes.
  • Timeline reconstruction improves downstream processing by emphasizing temporal relationships.
  • Clustering visualizes data spread and aids in understanding correlations.

Conclusions:

  • Exploratory analysis tools offer insights into clinical note preprocessing.
  • Enhanced data representation improves model performance and interpretability.
  • These advancements contribute to making data-driven medicine a reality.