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Related Concept Videos

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The treatment of pneumonia varies based on its severity and the causative pathogen. Here is a structured approach to managing pneumonia, integrating pharmaceutical and supportive care strategies.
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For bacterial pneumonia, antibiotics serve as the cornerstone of therapy. Initial treatment often begins with empirical antibiotics, tailored to the anticipated causative organism and adjusted based on culture results. Key antibiotic choices include:
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Related Experiment Video

Updated: Jul 12, 2025

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii
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A data-driven framework for clinical decision support applied to pneumonia management.

Robert C Free1, Daniel Lozano Rojas2, Matthew Richardson2

  • 1School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom.

Frontiers in Digital Health
|October 25, 2023
PubMed
Summary
This summary is machine-generated.

A new framework simplifies embedding artificial intelligence (AI) and machine learning (ML) clinical decision support tools into health systems. This enhances trust by visualizing AI predictions for managing community-acquired pneumonia, improving patient prioritization.

Keywords:
artificial intelligenceclinical decision supportdata-drivenmachine learningpneumonia

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Integrating clinical decision support (CDS) tools, especially those using machine learning (ML) and artificial intelligence (AI), into health information systems remains challenging.
  • Lack of transparency in complex AI/ML models, often perceived as "black boxes," can hinder user trust and adoption among healthcare professionals.
  • Community-acquired pneumonia (CAP) is a significant cause of in-hospital mortality, necessitating effective management strategies and decision support.

Purpose of the Study:

  • To present and evaluate a data-driven framework designed to overcome challenges in embedding AI/ML-based CDS tools.
  • To demonstrate the framework's application in developing and testing a CDS tool for in-hospital CAP management.
  • To illustrate how such tools can be integrated into clinical systems with enhanced visualization for improved understanding and trust.

Main Methods:

  • Developed a data-driven framework to address issues of plausibility and trust in AI/ML-based CDS.
  • Applied the framework to create a CDS tool for managing community-acquired pneumonia, aligned with local guidelines.
  • Integrated the CDS tool into a prototype clinical system, incorporating metrics and visualizations for patient journey and risk assessment.

Main Results:

  • The developed framework successfully moderated challenges associated with embedding complex AI/ML models into clinical workflows.
  • The CDS tool demonstrated effectiveness in retrospectively prioritizing patients for CAP management.
  • Integration into a prototype system provided decision-makers with visual insights into patient data, risk scores, and AI predictions.

Conclusions:

  • The data-driven framework shows significant potential for developing, testing, and evaluating workflow-integrated CDS tools that incorporate complex AI/ML models.
  • Visualizations and metrics derived from the framework enhance the interpretability and usability of AI/ML predictions for healthcare providers.
  • This approach facilitates the seamless embedding of advanced AI/ML capabilities into clinical systems, fostering trust and improving disease management.