Source: International Congress 2019 – M-health/e-health I
Disease area: -



Rating:

Share or cite this content

Citations should be made in the following way:
T. Grzywalski (Poznan, Poland), M. Szajek (Poznan, Poland), H. Hafke-Dys (Poznan, Poland), A. Breborowicz (Poznan, Poland), J. Kocinski (Poznan, Poland), A. Pastusiak (Poznan, Poland), R. Belluzzo (Poznan, Poland). Respiratory system auscultation using machine learning - a big step towards objectivisation?. 2231

Member's Comments

No comment yet.

Related content which might interest you: Late Breaking Abstract - Novel low-cost remote respiratory auscultation device vs. traditional stethoscope Source: Virtual Congress 2021 – E-health for COVID-19 Year: 2021 Introduction of a new approach to interpret pulmonary function tests (PFT) based on Machine learning and Game theory Source: International Congress 2019 – Assessment and management of immune-mediated interstitial lung diseases Year: 2019 Is it feasible for respiratory physiologists to carry out inhaler device technique checks during routine pulmonary function tests? Source: International Congress 2016 – Man versus machine: waves, frequency, and more in lung function Year: 2016 Late Breaking Abstract - Clinical value of a mandibular movements recording sensor in obstructive sleep apnea diagnosis: a machine learning approach Source: International Congress 2019 – Pathophysiological aspects of obstructive sleep apnoea from sea level to high altitude Year: 2019 Interfaces, ventilators and mode of ventilation in the home setting: what’s new? Source: ERS course 2015 Year: 2015 Interfaces, ventilators and mode of ventilation in the acute setting: what’s new? Source: ERS course 2015 Year: 2015 "Cooking the beast" microwaves: A new fast method for reopening the airways Source: International Congress 2016 – Airway stenting and ablation Year: 2016 Non-invasive assessment of VQ - should we use a two compartment or three compartment model? Source: International Congress 2014 – New aspects of lung function testing in children Year: 2014 Advanced roles in respiratory healthcare science: it's not just spirometry Source: Breathe, 15 (4) 267; 10.1183/20734735.0310-2019 Year: 2019 Late Breaking Abstract - Identifying and phenotyping COVID-19 patients using machine learning on chest x-rays Source: Virtual Congress 2020 – Covering COVID - the best abstracts Year: 2020 Effect of critical care-trained dedicated medical leadership on the delays at acute noninvasive ventilation (NIV) set ups at the "front door" Source: International Congress 2014 – Improving noninvasive ventilation efficacy Year: 2014 Late Breaking Abstract - Do face masks cause abnormal gas exchange during exercise testing in children? Source: Virtual Congress 2021 – Causes and consequences of paediatric respiratory diseases Year: 2021 How to use a mechanical insufflator–exsufflator "cough assist machine" Source: Breathe 2008; 4: 320-325 Year: 2008 Late Breaking Abstract - Technical validation of breath analysis by eNose in disease diagnosis: Tidal breathing vs. vital capacity manoeuvre Source: International Congress 2019 – Insights into physiological diagnostic services Year: 2019 Unravelling machine learning: insights in respiratory medicine Source: Eur Respir J, 54 (6) 1901216; 10.1183/13993003.01216-2019 Year: 2019 Troubleshooting - what to do when NIV is not going well? Source: School Course 2014 - Noninvasive ventilation: basic concepts Year: 2014 Interfaces and mode of ventilation in the home setting: what is new? Source: ERS Course 2018 - Noninvasive ventilation: advanced Year: 2018 Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19 Source: Eur Respir Rev, 29 (157) 200181; 10.1183/16000617.0181-2020 Year: 2020