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Acute Respiratory Failure-V

Acute Respiratory Failure-V

The treatment for acute respiratory failure varies based on factors like the underlying cause, overall health, and severity. A collaborative healthcare team is essential for early detection, often through arterial blood gas analysis. Identifying the cause is the primary goal, with treatment strategies adjusted for ventilation/perfusion (V/Q) mismatch, shunting, or diffusion impairment.
Ensure that patients are monitored continuously for their response to therapy, including changes in...

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Related Experiment Video

Updated: Dec 14, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

Siavash Bolourani¹, Ping Wang², Vihas M Patel³

¹The Feinstein Institute for Medical Research, Manhasset, NY; Elmezzi Graduate School of Molecular Medicine, Manhasset, NY; Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY; Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY.

|July 19, 2020

Summary

This summary is machine-generated.

Machine learning models can predict respiratory failure in post-pulmonary lobectomy patients. Identifying risk factors improves patient outcomes and standardizes quality care.

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Published on: October 10, 2018

Area of Science:

Thoracic surgery outcomes
Pulmonary complications
Machine learning in healthcare

Background:

Pulmonary complications post-lobectomy increase mortality, hospital stay, and readmissions.
Lack of consolidated clinical data hinders risk assessment for respiratory failure.
Standardizing outcome measures for thoracic surgery patients is challenging.

Purpose of the Study:

Identify risk factors for respiratory failure after pulmonary lobectomy.
Develop machine learning models to predict respiratory failure.
Enhance clinical decision-making and quality review for post-lobectomy patients.

Main Methods:

Utilized the 2015 Nationwide Inpatient Sample dataset.
Analyzed 4,062 patients who underwent pulmonary lobectomy, identifying 417 cases of respiratory failure.
Developed and optimized two machine learning models for respiratory failure prediction.

Main Results:

Identified key risk factors including pre-existing chronic diseases and intraoperative/postoperative events.
Generated two prediction models: one for performance evaluation (high accuracy/specificity) and one for clinical decision-making (high sensitivity).
Validated the models using clinical data from post-lobectomy patients.

Conclusions:

Established risk factors for respiratory failure post-lobectomy.
Introduced two machine learning techniques for predicting respiratory failure.
Proposed these techniques for targeted patient support and standardized quality peer review.