AIMC Topic: Respiratory Tract Diseases
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Implementation of digital home monitoring and management of respiratory disease.
Current opinion in pulmonary medicine
May 2, 2023
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
Jan 6, 2022
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
Aug 25, 2021
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
Mar 17, 2020
: 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
May 1, 2019
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
Mar 31, 2019
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...
Beijing
Respiratory Tract Diseases
Age Factors
Adolescent
Infant, Newborn
Particulate Matter
Humans
Acute Disease
Risk Factors
Incidence
Environmental Exposure
Young Adult
Female
Air Pollutants
Aged
Child, Preschool
Weather
Adult
Aged, 80 and over
Sex Factors
Child
Infant
Male
Deep Learning
Middle Aged
China
Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks.
IEEE transactions on medical imaging
Nov 14, 2018
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....
Deep Convolutional Neural Networks for Chest Diseases Detection.
Journal of healthcare engineering
Aug 1, 2018
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many me...
Assessing the impact of PM on respiratory disease using artificial neural networks.
Environmental pollution (Barking, Essex : 1987)
Jan 5, 2018
Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression mo...
Associations between management practices and within-pen prevalence of calf diarrhea and respiratory disease on dairy farms using automated milk feeders.
Journal of dairy science
Dec 28, 2017
Data on management practices used with automated milk feeders (AMF) are needed to identify factors associated with calf health in these systems. The objectives of this observational, longitudinal, cross-sectional study were to estimate the prevalence...
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