AIMC Topic: Respiratory Tract Diseases

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Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.

Respiratory research
The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pre...

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Sensors (Basel, Switzerland)
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice,...

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships.

Journal of breath research
Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-bas...

Implementation of digital home monitoring and management of respiratory disease.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: Digital respiratory monitoring interventions (e.g. smart inhalers and digital spirometers) can improve clinical outcomes and/or organizational efficiency, and the focus is shifting to sustainable implementation as an approach to de...

Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Environmental science and pollution research international
Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecastin...

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.

Scientific reports
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first a...

Artificial intelligence and machine learning in respiratory medicine.

Expert review of respiratory medicine
: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.: We aimed to identify and describe the studies published on the use of AI and ML in t...

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

Health information management : journal of the Health Information Management Association of Australia
BACKGROUND: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all...

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

International journal of environmental health research
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks.

IEEE transactions on medical imaging
Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions....