e-learning
resources
Madrid 2019
Tuesday, 01.10.2019
Innovations in primary care assessment and management
Login
Search all ERS
e-learning
resources
Disease Areas
Airways Diseases
Interstitial Lung Diseases
Respiratory Critical Care
Respiratory Infections
Paediatric Respiratory Diseases
Pulmonary Vascular Diseases
Sleep and Breathing Disorders
Thoracic Oncology
Events
International Congress
Courses
Webinars
Conferences
Research Seminars
Journal Clubs
Publications
Breathe
Monograph
ERJ
ERJ Open Research
ERR
European Lung White Book
Handbook Series
Guidelines
All ERS guidelines
e-learning
CME Online
Case reports
Short Videos
SpirXpert
Procedure Videos
CME tests
Reference Database of Respiratory Sounds
Radiology Image Challenge
Brief tobacco interventions
EU Projects
VALUE-Dx
ERN-LUNG
ECRAID
UNITE4TB
Disease Areas
Events
Publications
Guidelines
e-learning
EU Projects
Login
Search
How can the data derived Bayesian network model help for screening of respiratory diseases?
Y. Thorat (Pune , India), A. Anand (Cambridge , United States of America), R. Fletcher (Cambridge , United States of America), S. Pawar (Pune , India), V. Das (Pune , India), S. Salvi (Pune , India)
Source:
International Congress 2019 – Innovations in primary care assessment and management
Session:
Innovations in primary care assessment and management
Session type:
Thematic Poster
Number:
3999
Disease area:
Airway diseases
Rating:
You must
login
to grade this presentation.
Share or cite this content
Citations should be made in the following way:
Y. Thorat (Pune , India), A. Anand (Cambridge , United States of America), R. Fletcher (Cambridge , United States of America), S. Pawar (Pune , India), V. Das (Pune , India), S. Salvi (Pune , India). How can the data derived Bayesian network model help for screening of respiratory diseases?. 3999
You must
login
to share this Presentation/Article on Twitter, Facebook, LinkedIn or by email.
Member's Comments
No comment yet.
You must
Login
to comment this presentation.
Related content which might interest you:
Late Breaking Abstract - Implications of treatable traits and treatment choices on exacerbation risk in moderate-severe asthma
Management of Severe Asthma in Pediatric Patients by an Interdisciplinary Team in a Public Hospital Setting.
Muscle energy techniques for COPD patients: Effects on pulmonary function and activities of daily living
Related content which might interest you:
New uses of data: from big data to artificial intelligence and clinical decision tools in children with respiratory diseases
Source: Virtual Congress 2021 – New technologies for the management of children with respiratory diseases
Year: 2021
Can AI help in detecting respiratory diseases with incomplete lung function data?
Source: Virtual Congress 2021 – Primary care diagnosis and multimorbidities
Year: 2021
The respiratory physiome: clustering based on a comprehensive lung function assessment in patients with COPD
Source: International Congress 2018 – The value of lung function testing in various pathophysiological conditions
Year: 2018
Utilising data from single cell approaches to study existing and recently emerging lung diseases?
Source: Virtual Congress 2021 – Utilising data from single cell approaches to study existing and recently emerging lung diseases?
Year: 2021
Is it possible to predict asthma outcomes? A disease model for economic analysis.
Source: International Congress 2017 – Asthma management
Year: 2017
New, effective method to calculate parameters of respiratory system model used to interpret transfer impedance data
Source: Eur Respir J 2003; 22: Suppl. 45, 98s
Year: 2003
A general framework to implement predictive models for hospitalizations: Application to COPD
Source: International Congress 2017 – Best abstracts in the management of chronic respiratory diseases
Year: 2017
Prediction model of COPD acute exacerbation with big data by machine learning methods
Source: Virtual Congress 2020 – Prediction and management of outcomes in obstructive diseases
Year: 2020
A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: latent class analysis and partitioning around medoids methods.
Source: International Congress 2018 – Airway disease: recent discoveries
Year: 2018
Leveraging causal modeling to predict future COPD
Source: Virtual Congress 2021 – Prediction of exacerbations in patients with COPD
Year: 2021
New approaches to the understanding of complex chronic lung diseases
Source: ISSN=ISSN 1025-448x, ISBN=ISBN 1-904097-50-2, page=345
Year: 2006
Where do we stand with asthma phenotypes derived from data-driven methods? A systematic review
Source: International Congress 2019 – Respiratory function assessment in disease
Year: 2019
A new mathematical model to identify contacts with recent and remote latent tuberculosis
Source: ERJ Open Res, 5 (2) 00078-2019; 10.1183/23120541.00078-2019
Year: 2019
A basic prediction model with clinical parameters to diagnose lung cancer.
Source: Virtual Congress 2021 – Screening, diagnosis, management and prognosis of lung cancer
Year: 2021
A multivariate geo-statistical Bayesian model for analyzing the spatial prevalence of COPD: Implications on risk factors
Source: International Congress 2015 – Epidemiology of respiratory disease
Year: 2015
A new statistical diagnostic tool for respiratory diseases
Source: Virtual Congress 2020 – New and integrated tools for respiratory diagnosis and care
Year: 2020
Non-targeted integrative multi-omics approaches on sputum reveal potential diagnostic and monitoring biomarkers for respiratory diseases
Source: International Congress 2017 – Novel molecular and genetic targets in COPD
Year: 2017
Decision tree models as a classifier of endothelial function based on strength, pulmonary and cardiac function in COPD: Preliminary analysis.
Source: Virtual Congress 2020 – Tapas of respiratory physiotherapy
Year: 2020
Which modelling methodology is best?
Source: ERS Research Seminar 2017 – The impact of air pollution on respiratory health
Year: 2018
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking "Accept", you consent to the use of the cookies.
Accept