Decision tree models as a classifier of endothelial function based on strength, pulmonary and cardiac function in COPD: Preliminary analysis.

N. Schafauser (São Carlos, Brazil), J. Henriques. (Coimbra, Portugal), E. Kabbach. (São Carlos, Brazil), A. Heubel (São Carlos, Brazil), B. Oliveira. (São Carlos, Brazil), V. Di Lorenzo. (São Carlos, Brazil), M. Roscani. (São Carlos, Brazil), A. Borghi-Silva. (São Carlos, Brazil), R. Mendes. (São Carlos, Brazil)

Source: Virtual Congress 2020 – Tapas of respiratory physiotherapy
Session: Tapas of respiratory physiotherapy
Session type: E-poster session
Number: 2874
Disease area: Airway diseases

Congress or journal article abstractE-poster

Rating: 0
You must login to grade this presentation.

Share or cite this content

Citations should be made in the following way:
N. Schafauser (São Carlos, Brazil), J. Henriques. (Coimbra, Portugal), E. Kabbach. (São Carlos, Brazil), A. Heubel (São Carlos, Brazil), B. Oliveira. (São Carlos, Brazil), V. Di Lorenzo. (São Carlos, Brazil), M. Roscani. (São Carlos, Brazil), A. Borghi-Silva. (São Carlos, Brazil), R. Mendes. (São Carlos, Brazil). Decision tree models as a classifier of endothelial function based on strength, pulmonary and cardiac function in COPD: Preliminary analysis.. 2874

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:
Can recursive partitioning empirically derive a pulmonary function test interpretive algorithm
Source: International Congress 2015 – New developments in medical education, web and internet
Year: 2015


The accuracy of FEF25-75/FVC for primary classification of pulmonary function test
Source: Annual Congress 2010 - Quality measurement and improvement in lung function
Year: 2010


Diagnosis of pulmonary hypertension from MR image based computational models of pulmonary vascular haemodynamics and decision tree analysis
Source: International Congress 2015 – Pulmonary hypertension: novel clinical insights
Year: 2015


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

Consensus network analysis reveals pathways associated with lung function decline in both COPD and IPF
Source: International Congress 2017 – Cellular signalling pathways in pulmonary fibrosis
Year: 2017

Hierarchy of common lung function parameters in diagnosing COPD using discriminant analysis
Source: Annual Congress 2010 - New diagnostic approaches in respiratory function
Year: 2010


Explaining predictions of an automated pulmonary function test interpretation algorithm
Source: International Congress 2019 – M-health/e-health I
Year: 2019

Benchmarking of pulmonary function studies in a large regional pulmonary function laboratory.
Source: International Congress 2019 – Exploring the lungs in various pathophysiological conditions
Year: 2019

Asthma phenotypes determined by a novel fluctuation based clustering method using a time window of lung function observations
Source: International Congress 2015 – Understanding paediatric asthma and allergy: genetics and environment
Year: 2015

Prognostic value of modified classification based on lung function tests in idiopathic pulmonary fibrosis (IPF)
Source: Eur Respir J 2006; 28: Suppl. 50, 827s
Year: 2006

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

Factors associated with poor pulmonary function: cross-sectional analysis of data from the ERCF
Source: Eur Respir J 2001; 18: 298-305
Year: 2001



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




LATE-BREAKING ABSTRACT: The correlations of automated lung emphysema analysis software results with pulmonary function test
Source: International Congress 2016 – Mixed-up imaging of various chest diseases
Year: 2016


Clinically applicable machine learning prediction model for pulmonary hypertension due to left heart disease
Source: Virtual Congress 2021 – Non-pulmonary arterial hypertension pulmonary hypertension
Year: 2021



Impact of the use of a finite value of NO conductance on Hb in the interpretation of TLNO and TLCO transfer in homogenous groups of patients
Source: International Congress 2014 – Lung function testing: new findings and new approaches
Year: 2014


Prediction of exercise capacity using impulse oscillation system in patients with COPD
Source: Annual Congress 2013 –Monitoring lung function in airway diseases
Year: 2013


Comparison of regression models for developing spirometric reference values
Source: International Congress 2016 – Functional assessment of the airways
Year: 2016


Using different pulmonary function parameters for establising reversibility in COPD patients
Source: Eur Respir J 2006; 28: Suppl. 50, 808s
Year: 2006