AIMC Topic: Community-Acquired Pneumonia

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A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.

Scientific reports
Acute kidney injury is a common and critical complication in patients with community-acquired pneumonia who are admitted to intensive care units, substantially increasing their risk of short-term mortality. To enhance early clinical decision-making, ...

Development and validation of a machine learning model for critical progression risk in pediatric severe community-acquired pneumonia.

Scientific reports
This study aimed to utilize various machine learning algorithms to develop a predictive model for the progression of severe community-acquired pneumonia (SCAP) in children to critical severe community-acquired pneumonia (cSCAP). Retrospective analysi...

A machine learning model including pentraxin-3 as predictor of outcomes in community-acquired pneumonia.

Journal of translational medicine
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...

Divergent biological pathways distinguish community-acquired pneumonia from COVID-19 despite similar plasma cytokine profiles.

Respiratory research
BACKGROUND: Pulmonary infections, ranging from mild respiratory issues to severe multiorgan failure, pose a major global health threat. The immune response in community-acquired pneumonia (CAP) and COVID-19 influences disease severity and outcomes, b...

Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.

Scientific reports
This study investigated and validated all-cause in-hospital death prediction models for hospitalized pneumonia patients based on large-scale clinical data, including diagnoses, medication prescriptions, and laboratory test codes. Feature selection wa...

Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

Scientific reports
The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the develo...

Severe community-acquired pneumonia (sCAP): advances in management and future directions.

Thorax
Severe community-acquired pneumonia (sCAP) is a major global health challenge, with high morbidity and mortality, especially among patients requiring intensive care. Despite advancements in antimicrobial therapies and supportive care, sCAP remains a ...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...