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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

148
Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
148
Mechanical Ventilation III: Noninvasive Ventilation01:23

Mechanical Ventilation III: Noninvasive Ventilation

110
Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
Noninvasive Positive-Pressure Ventilation...
110
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

323
A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
323
Other Pulmonary Disorders01:17

Other Pulmonary Disorders

836
Respiratory disorders encompass a range of conditions with varying levels of severity. Asthma, marked by chronic airway inflammation and hypersensitivity, is one such condition. It can lead to airway obstruction due to factors like bronchial spasms, mucosal edema, increased mucus secretion, or epithelial damage. Asthma triggers are diverse, ranging from allergens to emotional upset, and treatment focuses on both immediate relief through bronchodilators and long-term inflammation suppression.
836
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Achieving Better Understanding Of Obstructive Sleep Apnea Treatment Effects On Cardiovascular Disease Outcomes Through Machine Learning Approaches: A Narrative Review.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Achieving Better Understanding Of Obstructive Sleep Apnea Treatment Effects On Cardiovascular Disease Outcomes Through Machine Learning Approaches: A Narrative Review.

Related Experiment Video

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

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Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review.

Oren Cohen1, Vaishnavi Kundel1, Philip Robson2

  • 1Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Journal of Clinical Medicine
|April 9, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
artificial intelligencecardiovascular diseaseethics in machine learning and artificial intelligenceheterogeneity of treatment effects

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Obstructive sleep apnea (OSA) research needs better precision. Machine learning and AI can identify patient subgroups and improve cardiovascular disease (CVD) risk reduction strategies.

Area of Science:

  • Cardiovascular Medicine
  • Sleep Medicine
  • Data Science

Background:

  • Obstructive sleep apnea (OSA) affects nearly a billion people globally, strongly linked to cardiovascular diseases (CVDs).
  • Current treatments for OSA have not consistently shown benefits in reducing CVD outcomes in clinical trials.
  • A key challenge is the imprecise definition of OSA and identifying patient subgroups who may benefit from therapy.

Purpose of the Study:

  • To review how advanced data science methods, including machine learning (ML) and artificial intelligence (AI), can enhance OSA research.
  • To explore the potential of these techniques in understanding OSA's impact on CVD and enabling precision medicine.
  • To highlight team science approaches utilizing diverse data sources for improved CVD risk stratification in OSA patients.

Main Methods:

machine learning
obstructive sleep apnea
  • Review of recent applications of advanced statistical methods and ML in OSA research.
  • Discussion of heterogeneous treatment effects modeling for identifying treatment response subgroups.
  • Integration of team science principles with big data analysis, including clinical data, polysomnography, proteomics, and imaging.

Main Results:

  • ML and AI are revealing diverse OSA endotypes and phenotypes, moving beyond the apnea-hypopnea index.
  • These methods offer improved risk stratification by analyzing interactions between OSA and comorbidities.
  • Heterogeneous treatment effects modeling can pinpoint subgroups with differential responses to OSA therapies.

Conclusions:

  • Advanced data science, ML, and AI are crucial for dissecting OSA's role in CVD.
  • Precision medicine approaches in OSA patients for CVD risk reduction can be significantly advanced.
  • Team science leveraging big data is essential for future breakthroughs in OSA and CVD research.