e-learning
resources
Madrid 2019
Tuesday, 01.10.2019
How do big data and classical epidemiology contribute to solving chronic respiratory ill health?
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
Traditional methods of epidemiology and how they complement what is learned from big data
D. Jarvis (London, United Kingdom)
Source:
International Congress 2019 – How do big data and classical epidemiology contribute to solving chronic respiratory ill health?
Session:
How do big data and classical epidemiology contribute to solving chronic respiratory ill health?
Session type:
Symposium
Number:
3767
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:
D. Jarvis (London, United Kingdom). Traditional methods of epidemiology and how they complement what is learned from big data. International Congress 2019 – How do big data and classical epidemiology contribute to solving chronic respiratory ill health?
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:
To code or not to code chronic pulmonary aspergillosis associated malnutrition in PMSI database: that is the problem…
The course of asthma in preschool children with asthma
Live from Rome: Definition, epidemiology and impact of severe asthma
Related content which might interest you:
From traditional bacteriology to rapid molecular methods: the revolution is going on
Source: Annual Congress 2012 - PG7 TB and MDR-/XDR-TB: what is new in diagnosis, treatment and follow-up (TB PAN-NET)
Year: 2012
Some principles and practices of genetic biobanking studies
Source: Eur Respir J 2009; 33: 419-425
Year: 2009
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
Linking clinical algorithms to precision medicine
Source: Virtual Congress 2020 – Precision endotyping of asthma: time for action
Year: 2020
Some current challenges in statistical methods to illuminate disease genetics
Source: Research Seminar 2009 - Post Genome Respiratory Epidemiology II - An interdisciplinary challenge
Year: 2008
Current TB-epidemic situation among children in Ukraine and introduction of methods of molecular epidemiology
Source: Eur Respir J 2006; 28: Suppl. 50, 15s
Year: 2006
The new Back to Basics section: emerging concepts in basic and translational medicine
Source: Eur Respir J 2014; 44: 297-298
Year: 2014
There is no rationale to still rely on outdated, biased tools for quantitative morphology in pulmonary research
Source: Eur Respir Rev 2006; 15: 105-106
Year: 2006
Applying ecological theories in lung microbiome research: lessons learned from microbial ecology and evolution?
Source: Eur Respir Monogr 2019; 83: 50-66
Year: 2019
Value of animal models. Lessons learnt from another therapeutic area: implications for respiratory disease
Source: Annual Congress 2007 - PG11 - Translation of animal models to human airway disease
Year: 2007
Device-generated data: current knowledge and perspectives
Source: 5th Sleep and Breathing Conference
Year: 2019
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
Respiratory monitoring: revisiting classical physiological principles with new tools
Source: Eur Respir J 2004; 24 : 718-719
Year: 2004
Which modelling methodology is best?
Source: ERS Research Seminar 2017 – The impact of air pollution on respiratory health
Year: 2018
Application of big data technology for COVID-19: Primary care big data
Source: ERS Course 2021 - COVID-19: State of the art
Year: 2021
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
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