Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pneumonia I: Introduction01:30

Pneumonia I: Introduction

302
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...
302
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

171
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:
171
Pneumonia III: Complications and Assessment01:30

Pneumonia III: Complications and Assessment

366
Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
366
Pneumonia V: Nursing management and Prevention01:30

Pneumonia V: Nursing management and Prevention

2.4K
Nursing management of pneumonia involves promoting airway patency, facilitating rest and conserving energy, encouraging fluid intake, maintaining nutrition, and educating patients.
The nurse must practice strict medical asepsis and adhere to infection control guidelines to minimize healthcare-associated infections.
Enhance airway patency
Position the patient correctly to facilitate drainage of the affected lung segments. Manual or mechanical percussion and vibration can also be employed....
2.4K

You might also read

Related Articles

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

Sort by
Same author

Variability in epilepsy polygenic risk prediction across Taiwanese population and clinical cohorts.

Epilepsia·2026
Same author

Association Between Electron-Shuttling Capacity and Neuroprotective Potential of Huang-Lian-Jie-Du-Tang: A Bioelectrochemical, Biochemical and Computational Study.

Current pharmaceutical design·2026
Same author

In Silico Evaluation of Bioactive Compounds Isolated from Scleroderma citrinum Against H1N1 and Acetylcholinesterase: A Post-COVID Perspective.

Current pharmaceutical design·2026
Same author

Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.

BMC medical informatics and decision making·2026
Same author

Electrochemical Analysis and in silico Evaluation of Tamarindus indica Linn Fruit Pulp Extract for Potential Antidepressant Effects.

Current pharmaceutical design·2026
Same author

Modified Berlin Score for predicting sleep apnea in patients with acute ischemic stroke.

BMC neurology·2026

Related Experiment Video

Updated: Aug 25, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

328

Application of machine learning and natural language processing for predicting stroke-associated pneumonia.

Hui-Chu Tsai1, Cheng-Yang Hsieh2,3, Sheng-Feng Sung4,5

  • 1Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan.

Frontiers in Public Health
|October 17, 2022
PubMed
Summary

Machine learning models using clinical notes and structured data significantly improve prediction of stroke-associated pneumonia (SAP). This approach outperforms traditional risk scores, offering a pathway for better patient risk stratification.

Keywords:
machine learningnatural language processingpneumoniapredictionrisk scorestroke

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Aug 25, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

328
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Area of Science:

  • Medical Informatics
  • Computational Medicine
  • Clinical Epidemiology

Background:

  • Stroke-associated pneumonia (SAP) is a significant complication following acute stroke.
  • Identifying high-risk patients is crucial for targeted interventions to reduce SAP incidence.

Purpose of the Study:

  • To evaluate the efficacy of machine learning (ML) and natural language processing (NLP) in predicting SAP.
  • To compare the predictive performance of ML models against established clinical risk scores.

Main Methods:

  • Utilized linked data from a hospital stroke registry and electronic health records.
  • Applied NLP to extract features from unstructured clinical notes.
  • Developed random forest ML models using structured variables and textual features.
  • Compared ML model performance (AUC) against A²DS², ISAN, PNA, and ACDD⁴ scores.

Main Results:

  • Among 5,913 stroke patients, 7.6% developed SAP within 7 days.
  • The ML model incorporating both textual and structured data achieved the highest AUC (0.840).
  • This combined ML model significantly outperformed all conventional risk scores (P < 0.05).

Conclusions:

  • ML models integrating clinical text and structured data offer superior prediction of SAP compared to existing risk scores.
  • The developed workflow is adaptable for healthcare organizations to implement predictive SAP risk assessment.