Source: 5th Sleep and Breathing Conference



Rating:

Share or cite this content

Citations should be made in the following way:
Jean-Louis Pépin. Using big data to generate actionable clinical intelligence in PAP treatment. 5th Sleep and Breathing Conference

Member's Comments

No comment yet.

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 From clinical trials to real-world data: New dimensions of patient benefits Source: International Congress 2015 – IgE-mediated asthma: New revelations and future insights Year: 2015 Missing data in IPF trials: do not let methodological issues undermine a major therapeutic breakthrough Source: Eur Respir J 2015; 46: 607-614 Year: 2015 Pirfenidone: an update on clinical trial data and insights from everyday practice Source: Eur Respir Rev 2014; 23: 111-117 Year: 2014 Do the current endpoints in IPF clinical trials allow good regulatory decisions to be made? Source: International Congress 2014 – Ceremony for the ERS Rare Pulmonary Disease Research Award 2014 followed by the symposium "Challenges in endpoints for rare lung diseases" Year: 2014 How to use monitoring data to make wise clinical decisions Source: International Congress 2015 – PG3 Cardiorespiratory monitoring in critically ill patients Year: 2015 Using big data to change healthcare: improving quality of care with routine data Source: International Congress 2016 – Primary Care Day: challenging times III Year: 2016 Large trials, new knowledge: the changing face of COPD management Source: Eur Respir J 2015; 45: 1692-1703 Year: 2015 Establishment of an integrated clinical network and comprehensive data warehouse for the conduct of comparative effectiveness research in COPD Source: Annual Congress 2011 - COPD: integrated care and rehabilitation Year: 2011 How can real-world data be used to improve outcomes? Source: Research Seminar 2021 – Unravelling severe exacerbations of COPD – CICERO an ERS CRC Year: 2021 Adding biological markers to COPD categorisation schemes: a way towards more personalised care? Source: Eur Respir J 2016; 47:1601-1605 Year: 2016 Some principles and practices of genetic biobanking studies Source: Eur Respir J 2009; 33: 419-425 Year: 2009 Is data quality more important than data quantity in the diagnosis of occupational asthma from serial PEF records? Source: Annual Congress 2010 - Occupational and environmental exposures and respiratory disease Year: 2010 A machine learning approach to suspect excessive inactivity in COPD patients using non-activity-related clinical data Source: Virtual Congress 2021 – New approaches to pulmonary rehabilitation and chronic care Year: 2021 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 Is there a need for ongoing support for the interpretation of spirometry results by general practitioners? Source: Eur Respir J 2006; 28: Suppl. 50, 740s Year: 2006 Revising our approach to conducting therapeutic clinical research in COPD Source: Eur Respir J, 51 (2) 1702712; 10.1183/13993003.02712-2017 Year: 2018 What requirements should a biomarker meet to be used in the clinical and epidemiological setting? Source: Annual Congress 2008 - Biomarkers in COPD - application in clinical and epidemiological studies Year: 2008 The NIVO score: can it help to improve noninvasive ventilation in daily clinical practice? Source: Eur Respir J, 58 (2) 2100336; 10.1183/13993003.00336-2021 Year: 2021