Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pneumonia III: Complications and Assessment

Pneumonia III: Complications and Assessment

Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:

Pneumonia IV: Management

Pneumonia IV: Management

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.
Bacterial Pneumonia Treatment
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:

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Pneumonia I: Introduction

Pneumonia I: Introduction

Pneumonia is an acute respiratory infection that targets the lungs, specifically the alveoli. These tiny air sacs, essential for oxygen exchange, become engorged with pus and fluid, severely hindering breathing, decreasing oxygen absorption, and causing significant pain and discomfort during respiration.
Risk Factors
Various factors influence the likelihood of developing pneumonia. Age plays a crucial role, with infants, children under two, and individuals over 65 at increased risk due to their...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Tailoring Zeolite Nanosheets via Protozeolite-Directed Growth for Enhanced Methanol-to-Propylene Conversion.

Journal of the American Chemical Society·2026

Same author

Response Regarding: Electroacupuncture Alleviates Hip Fracture-Induced Acute Lung Injury in Elderly Mice.

The Journal of surgical research·2026

Same author

The identification of potent nonopioid analgesics and their potential for perioperative use.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Analysis of disparate factors affecting cognitive function among populations with different educational levels: a large-scale longitudinal study.

Frontiers in psychology·2026

Same author

Liposomal bupivacaine: is it the "code" to reduce postoperative opioid consumption?

International journal of surgery (London, England)·2026

Same author

Suzetrigine for the Treatment of Acute Pain: Comment.

Anesthesiology·2025

Search research articles

Related Experiment Video

Updated: May 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

Bingbing Xiang¹, Yiran Liu², Shulan Jiao³

¹Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in Public Health

|December 27, 2024

Summary

This summary is machine-generated.

This study developed machine learning models to predict postoperative pneumonia (POP) in surgical patients. The general linear model showed the best performance, identifying key predictors for early diagnosis and intervention.

Keywords:

machine learning perioperative medicine postoperative pneumonia prediction model risk factors

More Related Videos

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Published on: June 28, 2018

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: May 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Published on: June 28, 2018

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Surgical Outcomes Research

Background:

Postoperative pneumonia (POP) is a significant complication of hospital-acquired pneumonia, increasing patient morbidity and mortality.
Developing accurate predictive models for POP is crucial for timely intervention in surgical patients.

Purpose of the Study:

To develop and validate machine learning models for predicting postoperative pneumonia (POP) in surgical patients.
To compare the performance of nine different machine learning algorithms for POP prediction.
To identify key predictive factors for early POP detection and management.

Main Methods:

Retrospective analysis of electronic medical records from 528 surgical patients (264 with POP, 264 controls).
Feature selection identified 5 significant predictors from an initial 47 variables.
Nine machine learning models were developed and validated, including General Linear Model, Random Forest, and Support Vector Machine.

Main Results:

The General Linear Model achieved the highest performance with an AUC of 0.877, accuracy of 0.82, and F1 score of 0.80.
Key predictors identified include duration of bed rest, unplanned re-operation, end-tidal CO2, postoperative albumin, and chest X-ray findings.
The incidence of POP was 1.54% among 17,190 surgical patients, associated with adverse outcomes.

Conclusions:

The General Linear Model, utilizing 5 common variables, effectively predicts postoperative pneumonia in the general surgical population.
This model can aid clinicians in early prediction and diagnosis, facilitating optimal patient care and treatment strategies.